40-Issue 1

Permanent URI for this collection

Editorial

Editorial

Hauser, Helwig
Benes, Bedrich
Articles

Temporally Dense Exploration of Moving and Deforming Shapes

Frey, S.
Articles

Physics‐based Pathline Clustering and Exploration

Nguyen, Duong B.
Zhang, Lei
Laramee, Robert S.
Thompson, David
Monico, Rodolfo Ostilla
Chen, Guoning
Articles

A Curvature and Density‐based Generative Representation of Shapes

Ye, Z.
Umetani, N.
Igarashi, T.
Hoffmann, T.
Articles

Turbulent Details Simulation for SPH Fluids via Vorticity Refinement

Liu, Sinuo
Wang, Xiaokun
Ban, Xiaojuan
Xu, Yanrui
Zhou, Jing
Kosinka, Jiří
Telea, Alexandru C.
Articles

Framework for Capturing and Editing of Anisotropic Effect Coatings

Filip, J.
Vávra, R.
Maile, F. J.
Kolafová, M.
Articles

Stochastic Volume Rendering of Multi‐Phase SPH Data

Piochowiak, M.
Rapp, T.
Dachsbacher, C.
Articles

Anisotropic Spectral Manifold Wavelet Descriptor

Li, Qinsong
Hu, Ling
Liu, Shengjun
Yang, Dangfu
Liu, Xinru
Articles

Towards Light‐Weight Portrait Matting via Parameter Sharing

Dai, Yutong
Lu, Hao
Shen, Chunhua
Articles

Optimizing LBVH‐Construction and Hierarchy‐Traversal to accelerate kNN Queries on Point Clouds using the GPU

Jakob, J.
Guthe, M.
Articles

Time‐Warped Foveated Rendering for Virtual Reality Headsets

Franke, Linus
Fink, Laura
Martschinke, Jana
Selgrad, Kai
Stamminger, Marc
Articles

Adaptive Compositing and Navigation of Variable Resolution Images

Licorish, C.
Faraj, N.
Summa, B.
Articles

A Modified Double Gyre with Ground Truth Hyperbolic Trajectories for Flow Visualization

Wolligandt, S.
Wilde, T.
Rössl, C.
Theisel, H.
Articles

Modelling Material Microstructure Using the Perlin Noise Function

Conde‐Rodríguez, F.
García‐Fernández, Á‐.L.
Torres, J.C.
Articles

ECHO: Extended Convolution Histogram of Orientations for Local Surface Description

Mitchel, Thomas W.
Rusinkiewicz, Szymon
Chirikjian, Gregory S.
Kazhdan, Michael
Articles

Wavelet‐based Heat Kernel Derivatives: Towards Informative Localized Shape Analysis

Kirgo, Maxime
Melzi, Simone
Patanè, Giuseppe
Rodolà, Emanuele
Ovsjanikov, Maks
Articles

Structural Analogy from a Single Image Pair

Benaim, S.
Mokady, R.
Bermano, A.
Wolf, L.
Articles

EMU: Efficient Muscle Simulation in Deformation Space

Modi, V.
Fulton, L.
Jacobson, A.
Sueda, S.
Levin, D.I.W.
Articles

Learning Part Generation and Assembly for Sketching Man‐Made Objects

Du, Dong
Zhu, Heming
Nie, Yinyu
Han, Xiaoguang
Cui, Shuguang
Yu, Yizhou
Liu, Ligang
Articles

Primitive Object Grasping for Finger Motion Synthesis

Hwang, Jae‐Pyung
Park, Gangrae
Suh, Il Hong
Kwon, Taesoo
Articles

Functionality‐Driven Musculature Retargeting

Ryu, Hoseok
Kim, Minseok
Lee, Seungwhan
Park, Moon Seok
Lee, Kyoungmin
Lee, Jehee
Articles

The State of the Art of Spatial Interfaces for 3D Visualization

Besançon, Lonni
Ynnerman, Anders
Keefe, Daniel F.
Yu, Lingyun
Isenberg, Tobias
Articles

Interactive Optimization of Generative Image Modelling using Sequential Subspace Search and Content‐based Guidance

Chong, Toby
Shen, I‐Chao
Sato, Issei
Igarashi, Takeo
Articles

ClipFlip : Multi‐view Clipart Design

Shen, I‐Chao
Liu, Kuan‐Hung
Su, Li‐Wen
Wu, Yu‐Ting
Chen, Bing‐Yu
Articles

SketchZooms: Deep Multi‐view Descriptors for Matching Line Drawings

Navarro, Pablo
Orlando, J. Ignacio
Delrieux, Claudio
Iarussi, Emmanuel
Articles

TopoAct: Visually Exploring the Shape of Activations in Deep Learning

Rathore, Archit
Chalapathi, Nithin
Palande, Sourabh
Wang, Bei
Articles

Path‐based Monte Carlo Denoising Using a Three‐Scale Neural Network

Lin, Weiheng
Wang, Beibei
Yang, Jian
Wang, Lu
Yan, Ling‐Qi
Erratum

Erratum: Non‐uniform subdivision surfaces with sharp features

Articles

Automatic Image Checkpoint Selection for Guider‐Follower Pedestrian Navigation

Kwan, K. C.
Fu, H.
Articles

Thin Cloud Removal for Single RGB Aerial Image

Song, Chengfang
Xiao, Chunxia
Zhang, Yeting
Sui, Haigang
Miscellaneous

Life cycle of SARS‐CoV‐2: from sketch to visualization in atomistic resolution

Issue Information

Issue Information

Erratum

Erratum: Adjustable Constrained Soft‐Tissue Dynamics



BibTeX (40-Issue 1)
                
@article{
10.1111:cgf.14206,
journal = {Computer Graphics Forum}, title = {{
Editorial}},
author = {
Hauser, Helwig
and
Benes, Bedrich
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14206}
}
                
@article{
10.1111:cgf.14092,
journal = {Computer Graphics Forum}, title = {{
Temporally Dense Exploration of Moving and Deforming Shapes}},
author = {
Frey, S.
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14092}
}
                
@article{
10.1111:cgf.14093,
journal = {Computer Graphics Forum}, title = {{
Physics‐based Pathline Clustering and Exploration}},
author = {
Nguyen, Duong B.
and
Zhang, Lei
and
Laramee, Robert S.
and
Thompson, David
and
Monico, Rodolfo Ostilla
and
Chen, Guoning
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14093}
}
                
@article{
10.1111:cgf.14094,
journal = {Computer Graphics Forum}, title = {{
A Curvature and Density‐based Generative Representation of Shapes}},
author = {
Ye, Z.
and
Umetani, N.
and
Igarashi, T.
and
Hoffmann, T.
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14094}
}
                
@article{
10.1111:cgf.14095,
journal = {Computer Graphics Forum}, title = {{
Turbulent Details Simulation for SPH Fluids via Vorticity Refinement}},
author = {
Liu, Sinuo
and
Wang, Xiaokun
and
Ban, Xiaojuan
and
Xu, Yanrui
and
Zhou, Jing
and
Kosinka, Jiří
and
Telea, Alexandru C.
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14095}
}
                
@article{
10.1111:cgf.14119,
journal = {Computer Graphics Forum}, title = {{
Framework for Capturing and Editing of Anisotropic Effect Coatings}},
author = {
Filip, J.
and
Vávra, R.
and
Maile, F. J.
and
Kolafová, M.
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14119}
}
                
@article{
10.1111:cgf.14121,
journal = {Computer Graphics Forum}, title = {{
Stochastic Volume Rendering of Multi‐Phase SPH Data}},
author = {
Piochowiak, M.
and
Rapp, T.
and
Dachsbacher, C.
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14121}
}
                
@article{
10.1111:cgf.14120,
journal = {Computer Graphics Forum}, title = {{
Anisotropic Spectral Manifold Wavelet Descriptor}},
author = {
Li, Qinsong
and
Hu, Ling
and
Liu, Shengjun
and
Yang, Dangfu
and
Liu, Xinru
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14120}
}
                
@article{
10.1111:cgf.14179,
journal = {Computer Graphics Forum}, title = {{
Towards Light‐Weight Portrait Matting via Parameter Sharing}},
author = {
Dai, Yutong
and
Lu, Hao
and
Shen, Chunhua
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14179}
}
                
@article{
10.1111:cgf.14177,
journal = {Computer Graphics Forum}, title = {{
Optimizing LBVH‐Construction and Hierarchy‐Traversal to accelerate kNN Queries on Point Clouds using the GPU}},
author = {
Jakob, J.
and
Guthe, M.
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14177}
}
                
@article{
10.1111:cgf.14176,
journal = {Computer Graphics Forum}, title = {{
Time‐Warped Foveated Rendering for Virtual Reality Headsets}},
author = {
Franke, Linus
and
Fink, Laura
and
Martschinke, Jana
and
Selgrad, Kai
and
Stamminger, Marc
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14176}
}
                
@article{
10.1111:cgf.14178,
journal = {Computer Graphics Forum}, title = {{
Adaptive Compositing and Navigation of Variable Resolution Images}},
author = {
Licorish, C.
and
Faraj, N.
and
Summa, B.
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14178}
}
                
@article{
10.1111:cgf.14183,
journal = {Computer Graphics Forum}, title = {{
A Modified Double Gyre with Ground Truth Hyperbolic Trajectories for Flow Visualization}},
author = {
Wolligandt, S.
and
Wilde, T.
and
Rössl, C.
and
Theisel, H.
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14183}
}
                
@article{
10.1111:cgf.14182,
journal = {Computer Graphics Forum}, title = {{
Modelling Material Microstructure Using the Perlin Noise Function}},
author = {
Conde‐Rodríguez, F.
and
García‐Fernández, Á‐.L.
and
Torres, J.C.
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14182}
}
                
@article{
10.1111:cgf.14181,
journal = {Computer Graphics Forum}, title = {{
ECHO: Extended Convolution Histogram of Orientations for Local Surface Description}},
author = {
Mitchel, Thomas W.
and
Rusinkiewicz, Szymon
and
Chirikjian, Gregory S.
and
Kazhdan, Michael
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14181}
}
                
@article{
10.1111:cgf.14180,
journal = {Computer Graphics Forum}, title = {{
Wavelet‐based Heat Kernel Derivatives: Towards Informative Localized Shape Analysis}},
author = {
Kirgo, Maxime
and
Melzi, Simone
and
Patanè, Giuseppe
and
Rodolà, Emanuele
and
Ovsjanikov, Maks
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14180}
}
                
@article{
10.1111:cgf.14186,
journal = {Computer Graphics Forum}, title = {{
Structural Analogy from a Single Image Pair}},
author = {
Benaim, S.
and
Mokady, R.
and
Bermano, A.
and
Wolf, L.
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14186}
}
                
@article{
10.1111:cgf.14185,
journal = {Computer Graphics Forum}, title = {{
EMU: Efficient Muscle Simulation in Deformation Space}},
author = {
Modi, V.
and
Fulton, L.
and
Jacobson, A.
and
Sueda, S.
and
Levin, D.I.W.
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14185}
}
                
@article{
10.1111:cgf.14184,
journal = {Computer Graphics Forum}, title = {{
Learning Part Generation and Assembly for Sketching Man‐Made Objects}},
author = {
Du, Dong
and
Zhu, Heming
and
Nie, Yinyu
and
Han, Xiaoguang
and
Cui, Shuguang
and
Yu, Yizhou
and
Liu, Ligang
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14184}
}
                
@article{
10.1111:cgf.14187,
journal = {Computer Graphics Forum}, title = {{
Primitive Object Grasping for Finger Motion Synthesis}},
author = {
Hwang, Jae‐Pyung
and
Park, Gangrae
and
Suh, Il Hong
and
Kwon, Taesoo
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14187}
}
                
@article{
10.1111:cgf.14191,
journal = {Computer Graphics Forum}, title = {{
Functionality‐Driven Musculature Retargeting}},
author = {
Ryu, Hoseok
and
Kim, Minseok
and
Lee, Seungwhan
and
Park, Moon Seok
and
Lee, Kyoungmin
and
Lee, Jehee
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14191}
}
                
@article{
10.1111:cgf.14189,
journal = {Computer Graphics Forum}, title = {{
The State of the Art of Spatial Interfaces for 3D Visualization}},
author = {
Besançon, Lonni
and
Ynnerman, Anders
and
Keefe, Daniel F.
and
Yu, Lingyun
and
Isenberg, Tobias
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14189}
}
                
@article{
10.1111:cgf.14188,
journal = {Computer Graphics Forum}, title = {{
Interactive Optimization of Generative Image Modelling using Sequential Subspace Search and Content‐based Guidance}},
author = {
Chong, Toby
and
Shen, I‐Chao
and
Sato, Issei
and
Igarashi, Takeo
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14188}
}
                
@article{
10.1111:cgf.14190,
journal = {Computer Graphics Forum}, title = {{
ClipFlip : Multi‐view Clipart Design}},
author = {
Shen, I‐Chao
and
Liu, Kuan‐Hung
and
Su, Li‐Wen
and
Wu, Yu‐Ting
and
Chen, Bing‐Yu
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14190}
}
                
@article{
10.1111:cgf.14197,
journal = {Computer Graphics Forum}, title = {{
SketchZooms: Deep Multi‐view Descriptors for Matching Line Drawings}},
author = {
Navarro, Pablo
and
Orlando, J. Ignacio
and
Delrieux, Claudio
and
Iarussi, Emmanuel
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14197}
}
                
@article{
10.1111:cgf.14195,
journal = {Computer Graphics Forum}, title = {{
TopoAct: Visually Exploring the Shape of Activations in Deep Learning}},
author = {
Rathore, Archit
and
Chalapathi, Nithin
and
Palande, Sourabh
and
Wang, Bei
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14195}
}
                
@article{
10.1111:cgf.14194,
journal = {Computer Graphics Forum}, title = {{
Path‐based Monte Carlo Denoising Using a Three‐Scale Neural Network}},
author = {
Lin, Weiheng
and
Wang, Beibei
and
Yang, Jian
and
Wang, Lu
and
Yan, Ling‐Qi
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14194}
}
                
@article{
10.1111:cgf.14193,
journal = {Computer Graphics Forum}, title = {{
Erratum: Non‐uniform subdivision surfaces with sharp features}},
author = {}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14193}
}
                
@article{
10.1111:cgf.14192,
journal = {Computer Graphics Forum}, title = {{
Automatic Image Checkpoint Selection for Guider‐Follower Pedestrian Navigation}},
author = {
Kwan, K. C.
and
Fu, H.
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14192}
}
                
@article{
10.1111:cgf.14196,
journal = {Computer Graphics Forum}, title = {{
Thin Cloud Removal for Single RGB Aerial Image}},
author = {
Song, Chengfang
and
Xiao, Chunxia
and
Zhang, Yeting
and
Sui, Haigang
}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14196}
}
                
@article{
10.1111:cgf.14200,
journal = {Computer Graphics Forum}, title = {{
Life cycle of SARS‐CoV‐2: from sketch to visualization in atomistic resolution}},
author = {}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14200}
}
                
@article{
10.1111:cgf.14039,
journal = {Computer Graphics Forum}, title = {{
Issue Information}},
author = {}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14039}
}
                
@article{
10.1111:cgf.14201,
journal = {Computer Graphics Forum}, title = {{
Erratum: Adjustable Constrained Soft‐Tissue Dynamics}},
author = {}, year = {
2021},
publisher = {
© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.14201}
}

Browse

Recent Submissions

Now showing 1 - 33 of 33
  • Item
    Editorial
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Hauser, Helwig; Benes, Bedrich; Benes, Bedrich and Hauser, Helwig
  • Item
    Temporally Dense Exploration of Moving and Deforming Shapes
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Frey, S.; Benes, Bedrich and Hauser, Helwig
    We present our approach for the dense visualization and temporal exploration of moving and deforming shapes from scientific experiments and simulations. Our image space representation is created by convolving a noise texture along shape contours (akin to LIC). Beyond indicating spatial structure via luminosity, we additionally use colour to depict time or classes of shapes via automatically customized maps. This representation summarizes temporal evolution, and provides the basis for interactive user navigation in the spatial and temporal domain in combination with traditional renderings. Our efficient implementation supports the quick and progressive generation of our representation in parallel as well as adaptive temporal splits to reduce overlap. We discuss and demonstrate the utility of our approach using 2D and 3D scalar fields from experiments and simulations.
  • Item
    Physics‐based Pathline Clustering and Exploration
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Nguyen, Duong B.; Zhang, Lei; Laramee, Robert S.; Thompson, David; Monico, Rodolfo Ostilla; Chen, Guoning; Benes, Bedrich and Hauser, Helwig
    Most existing unsteady flow visualization techniques concentrate on the depiction of geometric patterns in flow, assuming the geometry information provides sufficient representation of the underlying physical characteristics, which is not always the case. To address this challenge, this work proposes to analyse the time‐dependent characteristics of the physical attributes measured along pathlines which can be represented as a series of time activity curves (TAC). We demonstrate that the temporal trends of these TACs can convey the relation between pathlines and certain well‐known flow features (e.g. vortices and shearing layers), which enables us to select pathlines that can effectively represent the physical characteristics of interest and their temporal behaviour in the unsteady flow. Inspired by this observation, a new TAC‐based unsteady flow visualization and analysis framework is proposed. The centre of this framework is a new similarity measure that compares the similarity of two TACs, from which a new spatio‐temporal, hierarchical clustering that classifies pathlines based on their physical attributes, and a TAC‐based pathline exploration and selection strategy are proposed. A visual analytic system incorporating the TAC‐based pathline clustering and exploration is developed, which also provides new visualizations to support the user exploration of unsteady flow using TACs. This visual analytic system is applied to a number of unsteady flow in 2D and 3D to demonstrate its utility. The new system successfully reveals the detailed structure of vortices, the relation between shear layer and vortex formation, and vortex breakdown, which are difficult to convey with conventional methods.
  • Item
    A Curvature and Density‐based Generative Representation of Shapes
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Ye, Z.; Umetani, N.; Igarashi, T.; Hoffmann, T.; Benes, Bedrich and Hauser, Helwig
    This paper introduces a generative model for 3D surfaces based on a representation of shapes with mean curvature and metric, which are invariant under rigid transformation. Hence, compared with existing 3D machine learning frameworks, our model substantially reduces the influence of translation and rotation. In addition, the local structure of shapes will be more precisely captured, since the curvature is explicitly encoded in our model. Specifically, every surface is first conformally mapped to a canonical domain, such as a unit disk or a unit sphere. Then, it is represented by two functions: the mean curvature half‐density and the vertex density, over this canonical domain. Assuming that input shapes follow a certain distribution in a latent space, we use the variational autoencoder to learn the latent space representation. After the learning, we can generate variations of shapes by randomly sampling the distribution in the latent space. Surfaces with triangular meshes can be reconstructed from the generated data by applying isotropic remeshing and spin transformation, which is given by Dirac equation. We demonstrate the effectiveness of our model on datasets of man‐made and biological shapes and compare the results with other methods.
  • Item
    Turbulent Details Simulation for SPH Fluids via Vorticity Refinement
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Liu, Sinuo; Wang, Xiaokun; Ban, Xiaojuan; Xu, Yanrui; Zhou, Jing; Kosinka, Jiří; Telea, Alexandru C.; Benes, Bedrich and Hauser, Helwig
    A major issue in smoothed particle hydrodynamics (SPH) approaches is the numerical dissipation during the projection process, especially under coarse discretizations. High‐frequency details, such as turbulence and vortices, are smoothed out, leading to unrealistic results. To address this issue, we introduce a vorticity refinement (VR) solver for SPH fluids with negligible computational overhead. In this method, the numerical dissipation of the vorticity field is recovered by the difference between the theoretical and the actual vorticity, so as to enhance turbulence details. Instead of solving the Biot‐Savart integrals, a stream function, which is easier and more efficient to solve, is used to relate the vorticity field to the velocity field. We obtain turbulence effects of different intensity levels by changing an adjustable parameter. Since the vorticity field is enhanced according to the curl field, our method can not only amplify existing vortices, but also capture additional turbulence. Our VR solver is straightforward to implement and can be easily integrated into existing SPH methods.
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    Framework for Capturing and Editing of Anisotropic Effect Coatings
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Filip, J.; Vávra, R.; Maile, F. J.; Kolafová, M.; Benes, Bedrich and Hauser, Helwig
    Coatings are used today for products, ranging from automotive production to electronics and everyday use items. Product design is taking on an increasingly important role, where effect pigments come to the fore, offering a coated surface extra optical characteristics. Individual effect pigments have strong anisotropic, azimuthaly‐dependent behaviour, typically suppressed by a coating application process, randomly orienting pigment particles resulting in isotropic appearance. One exception is a pigment that allows control of the azimuthal orientation of flakes using a magnetic field. We investigate visual texture effects due to such an orientation in a framework allowing efficient capturing, modelling and editing of its appearance. We captured spatially‐varying BRDFs of four coatings containing magnetic effect pigments. As per‐pixel non‐linear fitting cannot preserve coating sparkle effects, we suggest a novel method of anisotropy modelling based on images shifting in an angular domain. The model can be utilized for a fast transfer of desired anisotropy to any isotropic effect coating, while preserving important spatially‐varying visual features of the original coating. The anisotropic behaviour was fitted by a parametric model allowing for editing of coating appearance. This framework allows exploration of anisotropic effect coatings and their appearance transfer to standard effect coatings in a virtual environment.
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    Stochastic Volume Rendering of Multi‐Phase SPH Data
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Piochowiak, M.; Rapp, T.; Dachsbacher, C.; Benes, Bedrich and Hauser, Helwig
    In this paper, we present a novel method for the direct volume rendering of large smoothed‐particle hydrodynamics (SPH) simulation data without transforming the unstructured data to an intermediate representation. By directly visualizing the unstructured particle data, we avoid long preprocessing times and large storage requirements. This enables the visualization of large, time‐dependent, and multivariate data both as a post‐process and in situ. To address the computational complexity, we introduce stochastic volume rendering that considers only a subset of particles at each step during ray marching. The sample probabilities for selecting this subset at each step are thereby determined both in a view‐dependent manner and based on the spatial complexity of the data. Our stochastic volume rendering enables us to scale continuously from a fast, interactive preview to a more accurate volume rendering at higher cost. Lastly, we discuss the visualization of free‐surface and multi‐phase flows by including a multi‐material model with volumetric and surface shading into the stochastic volume rendering.
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    Anisotropic Spectral Manifold Wavelet Descriptor
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Li, Qinsong; Hu, Ling; Liu, Shengjun; Yang, Dangfu; Liu, Xinru; Benes, Bedrich and Hauser, Helwig
    In this paper, we present a powerful spectral shape descriptor for shape analysis, named Anisotropic Spectral Manifold Wavelet Descriptor (ASMWD). We proposed a novel manifold harmonic signal processing tool termed Anisotropic Spectral Manifold Wavelet Transform (ASMWT) first. ASMWT allows to comprehensively analyse signals from multiple wavelet diffusion directions on local manifold regions of the shape with a series of low‐pass and band‐pass frequency filters in each direction. Based on the ASMWT coefficients of a very simple signal, the ASMWD is efficiently constructed as a localizable and discriminative multi‐scale point descriptor. Since the wavelets used in our descriptor are direction‐sensitive and able to robustly reconstruct the signals with a finite number of scales, it makes our descriptor compact, efficient, and unambiguous under intrinsic symmetry. The extensive experiments demonstrate that our descriptor achieves significantly better performance than the state‐of‐the‐art descriptors and can greatly improve the performance of shape matching methods including both handcrafted and learning‐based methods.
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    Towards Light‐Weight Portrait Matting via Parameter Sharing
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Dai, Yutong; Lu, Hao; Shen, Chunhua; Benes, Bedrich and Hauser, Helwig
    Traditional portrait matting methods typically consist of a trimap estimation network and a matting network. Here, we propose a new light‐weight portrait matting approach, termed parameter‐sharing portrait matting (PSPM). Different from conventional portrait matting models where the encoder and decoder networks in two tasks are often separately designed, here a single encoder is employed for the two tasks in PSPM, while each task still has its task‐specific decoder. Thus, the role of the encoder is to extract semantic features and two decoders function as a bridge between low‐resolution feature maps generated by the encoder and high‐resolution feature maps for pixel‐wise classification/regression. In particular, three variants capable of implementing the parameter‐sharing portrait matting network are proposed and investigated, respectively. As demonstrated in our experiments, model capacity and computation costs can be reduced significantly, by up to and , respectively, with PSPM, whereas the matting accuracy only slightly deteriorates. In addition, qualitative and quantitative evaluations show that sharing the encoder is an effective way to achieve portrait matting with limited computational budgets, indicating a promising direction for applications of real‐time portrait matting on mobile devices.
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    Optimizing LBVH‐Construction and Hierarchy‐Traversal to accelerate kNN Queries on Point Clouds using the GPU
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Jakob, J.; Guthe, M.; Benes, Bedrich and Hauser, Helwig
    Processing point clouds often requires information about the point neighbourhood in order to extract, calculate and determine characteristics. We continue the tradition of developing increasingly faster neighbourhood query algorithms and present a highly efficient algorithm for solving the exact neighbourhood problem in point clouds using the GPU. Both, the required data structures and the NN query, are calculated entirely on the GPU. This enables real‐time performance for large queries in extremely large point clouds. Our experiments show a more than threefold acceleration, compared to state‐of‐the‐art GPU based methods including all memory transfers. In terms of pure query performance, we achieve over answered neighbourhood queries per millisecond for 16 nearest neighbours on common graphics hardware.
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    Time‐Warped Foveated Rendering for Virtual Reality Headsets
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Franke, Linus; Fink, Laura; Martschinke, Jana; Selgrad, Kai; Stamminger, Marc; Benes, Bedrich and Hauser, Helwig
    Rendering in real time for virtual reality headsets with high user immersion is challenging due to strict framerate constraints as well as due to a low tolerance for artefacts. Eye tracking‐based foveated rendering presents an opportunity to strongly increase performance without loss of perceived visual quality. To this end, we propose a novel foveated rendering method for virtual reality headsets with integrated eye tracking hardware. Our method comprises recycling pixels in the periphery by spatio‐temporally reprojecting them from previous frames. Artefacts and disocclusions caused by this reprojection are detected and re‐evaluated according to a confidence value that is determined by a newly introduced formalized perception‐based metric, referred to as confidence function. The foveal region, as well as areas with low confidence values, are redrawn efficiently, as the confidence value allows for the delicate regulation of hierarchical geometry and pixel culling. Hence, the average primitive processing and shading costs are lowered dramatically. Evaluated against regular rendering as well as established foveated rendering methods, our approach shows increased performance in both cases. Furthermore, our method is not restricted to static scenes and provides an acceleration structure for post‐processing passes.
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    Adaptive Compositing and Navigation of Variable Resolution Images
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Licorish, C.; Faraj, N.; Summa, B.; Benes, Bedrich and Hauser, Helwig
    We present a new, high‐quality compositing pipeline and navigation approach for variable resolution imagery. The motivation of this work is to explore the use of variable resolution images as a quick and accessible alternative to traditional gigapixel mosaics. Instead of the common tedious acquisition of many images using specialized hardware, variable resolution images can achieve similarly deep zooms as large mosaics, but with only a handful of images. For this approach to be a viable alternative, the state‐of‐the‐art in variable resolution compositing needs to be improved to match the high‐quality approaches commonly used in mosaic compositing. To this end, we provide a novel, variable resolution mosaic seam calculation and gradient domain color correction. This approach includes a new priority order graph cuts computation along with a practical data structure to keep memory overhead low. In addition, navigating variable resolution images is challenging, especially at the zoom factors targeted in this work. To address this challenge, we introduce a new image interaction for variable resolution imagery: a pan that automatically, and smoothly, hugs available resolution. Finally, we provide several real‐world examples of our approach producing high‐quality variable resolution mosaics with deep zooms typically associated with gigapixel photography.
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    A Modified Double Gyre with Ground Truth Hyperbolic Trajectories for Flow Visualization
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Wolligandt, S.; Wilde, T.; Rössl, C.; Theisel, H.; Benes, Bedrich and Hauser, Helwig
    The model of a flow by Shadden et al. is a standard benchmark data set for the computation of hyperbolic Lagrangian Coherent Structures (LCS) in flow data. While structurally extremely simple, it generates hyperbolic LCS of arbitrary complexity. Unfortunately, the does not come with a well‐defined ground truth: the location of hyperbolic LCS boundaries can only be approximated by numerical methods that usually involve the gradient of the flow map. We present a new benchmark data set that is a small but carefully designed modification of the , which comes with ground truth closed‐form hyperbolic trajectories. This allows for computing hyperbolic LCS boundaries by a simple particle integration without the consideration of the flow map gradient. We use these hyperbolic LCS as a ground truth solution for testing an existing numerical approach for extracting hyperbolic trajectories. In addition, we are able to construct hyperbolic LCS curves that are significantly longer than in existing numerical methods.
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    Modelling Material Microstructure Using the Perlin Noise Function
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Conde‐Rodríguez, F.; García‐Fernández, Á‐.L.; Torres, J.C.; Benes, Bedrich and Hauser, Helwig
    This paper introduces a precise and easy to use method for defining the microstructure of heterogeneous solids. This method is based on the concept of Heterogeneous Composite Bézier Hyperpatch, and allows to accurately represent the primary material proportions, as well as the size and shape of the material phases. The solid microstructure is modelled using two functions: a material distribution function (to compute the portion of the solid volume occupied by each primary material), and a modified Perlin noise function that determines the shape and size of each primary material phase.With this method, the position and orientation of the solid in the modeling space does not affect the portion of its volume that is occupied by each primary material, nor the shape and size of the phases. However, the solid microstructure is coherently and automatically modified when the shape of the solid is edited. Regarding continuity, this method allows to define to which extent continuity (both in shape and material distribution) has to be preserved at the junction of the cells that compose the solid. This makes modeling geometrically complex figures very easy.
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    ECHO: Extended Convolution Histogram of Orientations for Local Surface Description
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Mitchel, Thomas W.; Rusinkiewicz, Szymon; Chirikjian, Gregory S.; Kazhdan, Michael; Benes, Bedrich and Hauser, Helwig
    This paper presents a novel, highly distinctive and robust local surface feature descriptor. Our descriptor is predicated on a simple observation: instead of describing the points in the vicinity of a feature point relative to a reference frame at the feature point, all points in the region describe the feature point relative to their own frames. Isometry invariance is a byproduct of this construction. Our descriptor is derived relative to the extended convolution – a generalization of the standard convolution that allows the filter to adaptively transform as it passes over the domain. As such, we name our descriptor the Extended Convolution Histogram of Orientations (ECHO). It exhibits superior performance compared to popular surface descriptors in both feature matching and shape correspondence experiments. In particular, the ECHO descriptor is highly stable under near‐isometric deformations and remains distinctive under significant levels of noise, tessellation, complex deformations and the kinds of interference commonly found in real data.
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    Wavelet‐based Heat Kernel Derivatives: Towards Informative Localized Shape Analysis
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Kirgo, Maxime; Melzi, Simone; Patanè, Giuseppe; Rodolà, Emanuele; Ovsjanikov, Maks; Benes, Bedrich and Hauser, Helwig
    In this paper, we propose a new construction for the Mexican hat wavelets on shapes with applications to partial shape matching. Our approach takes its main inspiration from the well‐established methodology of diffusion wavelets. This novel construction allows us to rapidly compute a multi‐scale family of Mexican hat wavelet functions, by approximating the derivative of the heat kernel. We demonstrate that this leads to a family of functions that inherit many attractive properties of the heat kernel (e.g. local support, ability to recover isometries from a single point, efficient computation). Due to its natural ability to encode high‐frequency details on a shape, the proposed method reconstructs and transfers ‐functions more accurately than the Laplace‐Beltrami eigenfunction basis and other related bases. Finally, we apply our method to the challenging problems of partial and large‐scale shape matching. An extensive comparison to the state‐of‐the‐art shows that it is comparable in performance, while both simpler and much faster than competing approaches.
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    Structural Analogy from a Single Image Pair
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Benaim, S.; Mokady, R.; Bermano, A.; Wolf, L.; Benes, Bedrich and Hauser, Helwig
    The task of unsupervised image‐to‐image translation has seen substantial advancements in recent years through the use of deep neural networks. Typically, the proposed solutions learn the characterizing distribution of two large, unpaired collections of images, and are able to alter the appearance of a given image, while keeping its geometry intact. In this paper, we explore the capabilities of neural networks to understand image given only a single pair of images, and . We seek to generate images that are : that is, to generate an image that keeps the appearance and style of , but has a structural arrangement that corresponds to . The key idea is to map between image patches at different scales. This enables controlling the granularity at which analogies are produced, which determines the conceptual distinction between style and content. In addition to , our method can be used to generate high quality imagery in other conditional generation tasks utilizing images and only: guided image synthesis, style and texture transfer, text translation as well as video translation. Our code and additional results are available in
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    EMU: Efficient Muscle Simulation in Deformation Space
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Modi, V.; Fulton, L.; Jacobson, A.; Sueda, S.; Levin, D.I.W.; Benes, Bedrich and Hauser, Helwig
    EMU is an efficient and scalable model to simulate bulk musculoskeletal motion with heterogenous materials. First, EMU requires no model reductions, or geometric coarsening, thereby producing results visually accurate when compared to an FEM simulation. Second, EMU is efficient and scales much better than state‐of‐the‐art FEM with the number of elements in the mesh, and is more easily parallelizable. Third, EMU can handle heterogeneously stiff meshes with an arbitrary constitutive model, thus allowing it to simulate soft muscles, stiff tendons and even stiffer bones all within one unified system. These three key characteristics of EMU enable us to efficiently orchestrate muscle activated skeletal movements. We demonstrate the efficacy of our approach via a number of examples with tendons, muscles, bones and joints.
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    Learning Part Generation and Assembly for Sketching Man‐Made Objects
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Du, Dong; Zhu, Heming; Nie, Yinyu; Han, Xiaoguang; Cui, Shuguang; Yu, Yizhou; Liu, Ligang; Benes, Bedrich and Hauser, Helwig
    Modeling 3D objects on existing software usually requires a heavy amount of interactions, especially for users who lack basic knowledge of 3D geometry. Sketch‐based modeling is a solution to ease the modelling procedure and thus has been researched for decades. However, modelling a man‐made shape with complex structures remains challenging. Existing methods adopt advanced deep learning techniques to map holistic sketches to 3D shapes. They are still bottlenecked to deal with complicated topologies. In this paper, we decouple the task of sketch2shape into a part generation module and a part assembling module, where deep learning methods are leveraged for the implementation of both modules. By changing the focus from holistic shapes to individual parts, it eases the learning process of the shape generator and guarantees high‐quality outputs. With the learned automated part assembler, users only need a little manual tuning to obtain a desired layout. Extensive experiments and user studies demonstrate the usefulness of our proposed system.
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    Primitive Object Grasping for Finger Motion Synthesis
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Hwang, Jae‐Pyung; Park, Gangrae; Suh, Il Hong; Kwon, Taesoo; Benes, Bedrich and Hauser, Helwig
    We developed a new framework to generate hand and finger grasping motions. The proposed framework provides online adaptation to the position and orientation of objects and can generate grasping motions even when the object shape differs from that used during motion capture. This is achieved by using a mesh model, which we call primitive object grasping (POG), to represent the object grasping motion. The POG model uses a mesh deformation algorithm that keeps the original shape of the mesh while adapting to varying constraints. These characteristics are beneficial for finger grasping motion synthesis that satisfies constraints for mimicking the motion capture sequence and the grasping points reflecting the shape of the object. We verify the adaptability of the proposed motion synthesizer according to its position/orientation and shape variations of different objects by using motion capture sequences for grasping primitive objects, namely, a sphere, a cylinder, and a box. In addition, a different grasp strategy called a three‐finger grasp is synthesized to validate the generality of the POG‐based synthesis framework.
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    Functionality‐Driven Musculature Retargeting
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Ryu, Hoseok; Kim, Minseok; Lee, Seungwhan; Park, Moon Seok; Lee, Kyoungmin; Lee, Jehee; Benes, Bedrich and Hauser, Helwig
    We present a novel retargeting algorithm that transfers the musculature of a reference anatomical model to new bodies with different sizes, body proportions, muscle capability, and joint range of motion while preserving the functionality of the original musculature as closely as possible. The geometric configuration and physiological parameters of musculotendon units are estimated and optimized to adapt to new bodies. The range of motion around joints is estimated from a motion capture dataset and edited further for individual models. The retargeted model is simulation‐ready, so we can physically simulate muscle‐actuated motor skills with the model. Our system is capable of generating a wide variety of anatomical bodies that can be simulated to walk, run, jump and dance while maintaining balance under gravity. We will also demonstrate the construction of individualized musculoskeletal models from bi‐planar X‐ray images and medical examination.
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    The State of the Art of Spatial Interfaces for 3D Visualization
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Besançon, Lonni; Ynnerman, Anders; Keefe, Daniel F.; Yu, Lingyun; Isenberg, Tobias; Benes, Bedrich and Hauser, Helwig
    We survey the state of the art of spatial interfaces for 3D visualization. Interaction techniques are crucial to data visualization processes and the visualization research community has been calling for more research on interaction for years. Yet, research papers focusing on interaction techniques, in particular for 3D visualization purposes, are not always published in visualization venues, sometimes making it challenging to synthesize the latest interaction and visualization results. We therefore introduce a taxonomy of interaction technique for 3D visualization. The taxonomy is organized along two axes: the primary source of input on the one hand and the visualization task they support on the other hand. Surveying the state of the art allows us to highlight specific challenges and missed opportunities for research in 3D visualization. In particular, we call for additional research in: (1) controlling 3D visualization widgets to help scientists better understand their data, (2) 3D interaction techniques for dissemination, which are under‐explored yet show great promise for helping museum and science centers in their mission to share recent knowledge, and (3) developing new measures that move beyond traditional time and errors metrics for evaluating visualizations that include spatial interaction.
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    Interactive Optimization of Generative Image Modelling using Sequential Subspace Search and Content‐based Guidance
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Chong, Toby; Shen, I‐Chao; Sato, Issei; Igarashi, Takeo; Benes, Bedrich and Hauser, Helwig
    Generative image modeling techniques such as GAN demonstrate highly convincing image generation result. However, user interaction is often necessary to obtain desired results. Existing attempts add interactivity but require either tailored architectures or extra data. We present a human‐in‐the‐optimization method that allows users to directly explore and search the latent vector space of generative image modelling. Our system provides multiple candidates by sampling the latent vector space, and the user selects the best blending weights within the subspace using multiple sliders. In addition, the user can express their intention through image editing tools. The system samples latent vectors based on inputs and presents new candidates to the user iteratively. An advantage of our formulation is that one can apply our method to arbitrary pre‐trained model without developing specialized architecture or data. We demonstrate our method with various generative image modelling applications, and show superior performance in a comparative user study with prior art iGAN [ZKSE16].
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    ClipFlip : Multi‐view Clipart Design
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Shen, I‐Chao; Liu, Kuan‐Hung; Su, Li‐Wen; Wu, Yu‐Ting; Chen, Bing‐Yu; Benes, Bedrich and Hauser, Helwig
    We present an assistive system for clipart design by providing visual scaffolds from the unseen viewpoints. Inspired by the artists' creation process, our system constructs the visual scaffold by first synthesizing the reference 3D shape of the input clipart and rendering it from the desired viewpoint. The critical challenge of constructing this visual scaffold is to generate a reference 3D shape that matches the user's expectations in terms of object sizing and positioning while preserving the geometric style of the input clipart. To address this challenge, we propose a user‐assisted curve extrusion method to obtain the reference 3D shape. We render the synthesized reference 3D shape with a consistent style into the visual scaffold. By following the generated visual scaffold, the users can efficiently design clipart with their desired viewpoints. The user study conducted by an intuitive user interface and our generated visual scaffold suggests that our system is especially useful for estimating the ratio and scale between object parts and can save on average 57% of drawing time.
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    SketchZooms: Deep Multi‐view Descriptors for Matching Line Drawings
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Navarro, Pablo; Orlando, J. Ignacio; Delrieux, Claudio; Iarussi, Emmanuel; Benes, Bedrich and Hauser, Helwig
    Finding point‐wise correspondences between images is a long‐standing problem in image analysis. This becomes particularly challenging for sketch images, due to the varying nature of human drawing style, projection distortions and viewport changes. In this paper, we present the first attempt to obtain a learned descriptor for dense registration in line drawings. Based on recent deep learning techniques for corresponding photographs, we designed descriptors to locally match image pairs where the object of interest belongs to the same semantic category, yet still differ drastically in shape, form, and projection angle. To this end, we have specifically crafted a data set of synthetic sketches using non‐photorealistic rendering over a large collection of part‐based registered 3D models. After training, a neural network generates descriptors for every pixel in an input image, which are shown togeneralize correctly in unseen sketches hand‐drawn by humans. We evaluate our method against a baseline of correspondences data collected from expert designers, in addition to comparisons with other descriptors that have been proven effective in sketches. Code, data and further resources will be publicly released by the time of publication.
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    TopoAct: Visually Exploring the Shape of Activations in Deep Learning
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Rathore, Archit; Chalapathi, Nithin; Palande, Sourabh; Wang, Bei; Benes, Bedrich and Hauser, Helwig
    Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e.combinations of neuron firings, at various layers of the network in response to a particular input. With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. In particular, we develop visualizations that show the shape of the activation space, the organizational principle behind neuron activations, and the relationships of these activations within a layer. Applying tools from topological data analysis, we present , a visual exploration system to study topological summaries of activation vectors. We present exploration scenarios using that provide valuable insights into learned representations of neural networks. We expect to give a topological perspective that enriches the current toolbox of neural network analysis, and to provide a basis for network architecture diagnosis and data anomaly detection.
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    Path‐based Monte Carlo Denoising Using a Three‐Scale Neural Network
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Lin, Weiheng; Wang, Beibei; Yang, Jian; Wang, Lu; Yan, Ling‐Qi; Benes, Bedrich and Hauser, Helwig
    Monte Carlo rendering is widely used in the movie industry. Since it is costly to produce noise‐free results directly, Monte Carlo denoising is often applied as a post‐process. Recently, deep learning methods have been successfully leveraged in Monte Carlo denoising. They are able to produce high quality denoised results, even with very low sample rate, e.g. 4 spp (sample per pixel). However, for difficult scene configurations, some details could be blurred in the denoised results. In this paper, we aim at preserving more details from inputs rendered with low spp. We propose a novel denoising pipeline that handles three‐scale features ‐ pixel, sample and path ‐ to preserve sharp details, uses an improved Res2Net feature extractor to reduce the network parameters and a smooth feature attention mechanism to remove low‐frequency splotches. As a result, our method achieves higher denoising quality and preserves better details than the previous methods.
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    Erratum: Non‐uniform subdivision surfaces with sharp features
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Benes, Bedrich and Hauser, Helwig
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    Automatic Image Checkpoint Selection for Guider‐Follower Pedestrian Navigation
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Kwan, K. C.; Fu, H.; Benes, Bedrich and Hauser, Helwig
    In recent years guider‐follower approaches show a promising solution to the challenging problem of last‐mile or indoor pedestrian navigation without micro‐maps or indoor floor plans for path planning. However, the success of such guider‐follower approaches is highly dependent on a set of manually and carefully chosen image or video checkpoints. This selection process is tedious and error‐prone. To address this issue, we first conduct a pilot study to understand how users as guiders select critical checkpoints from a video recorded while walking along a route, leading to a set of criteria for automatic checkpoint selection. By using these criteria, including visibility, stairs and clearness, we then implement this automation process. The key behind our technique is a lightweight, effective algorithm using left‐hand‐side and right‐hand‐side objects for path occlusion detection, which benefits both automatic checkpoint selection and occlusion‐aware path annotation on selected image checkpoints. Our experimental results show that our automatic checkpoint selection method works well in different navigation scenarios. The quality of automatically selected checkpoints is comparable to that of manually selected ones and higher than that of checkpoints by alternative automatic methods.
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    Thin Cloud Removal for Single RGB Aerial Image
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Song, Chengfang; Xiao, Chunxia; Zhang, Yeting; Sui, Haigang; Benes, Bedrich and Hauser, Helwig
    Acquired above variable clouds, aerial images contain the components of ground reflection and cloud effect. Due to the non‐uniformity, clouds in aerial images are even harder to remove than haze in terrestrial images. This paper proposes a divide‐and‐conquer scheme to remove the thin translucent clouds in a single RGB aerial image. Based on colour attenuation prior, we design a kind of veiling metric that indicates the local concentration of clouds effectively. By this metric, an aerial image containing thickness‐varied clouds is segmented into multiple regions. Each region is veiled by clouds of nearly‐equal concentration, and hence subject to common assumptions, such as boundary constraint on transmission. The atmospheric light in each region is estimated by the modified local colour‐line model and composed into a spatially‐varying airlight map for the entire image. Then scene transmission is estimated and further refined by a weighted ‐norm based contextual regularization. Finally, we recover ground reflection via the atmospheric scattering model. We verify our cloud removal method on a number of aerial images containing thin clouds and compare our results with classical single‐image dehazing methods and the state‐of‐the‐art learning‐based declouding method, respectively.
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    Life cycle of SARS‐CoV‐2: from sketch to visualization in atomistic resolution
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Benes, Bedrich and Hauser, Helwig
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    Issue Information
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Benes, Bedrich and Hauser, Helwig
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    Erratum: Adjustable Constrained Soft‐Tissue Dynamics
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Benes, Bedrich and Hauser, Helwig