42-Issue 2

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https://diglib.eg.org/handle/10.2312/2633295

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Stochastic Subsets for BVH Construction
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Tessari, Lorenzo; Dittebrandt, Addis; Doyle, Michael J.; Benthin, Carsten; Myszkowski, Karol; Niessner, Matthias
BVH construction is a critical component of real-time and interactive ray-tracing systems. However, BVH construction can be both compute and bandwidth intensive, especially when a large degree of dynamic geometry is present. Different build algorithms vary substantially in the traversal performance that they produce, making high quality construction algorithms desirable. However, high quality algorithms, such as top-down construction, are typically more expensive, limiting their benefit in real-time and interactive contexts. One particular challenge of high quality top-down construction algorithms is that the large working set at the top of the tree can make constructing these levels bandwidth-intensive, due to O(nlog(n)) complexity, limited cache locality, and less dense compute at these levels. To address this limitation, we propose a novel stochastic approach to GPU BVH construction that selects a representative subset to build the upper levels of the tree. As a second pass, the remaining primitives are clustered around the BVH leaves and further processed into a complete BVH. We show that our novel approach significantly reduces the construction time of top-down GPU BVH builders by a factor up to 1.8x, while achieving competitive rendering performance in most cases, and exceeding the performance in others.
Learning to Learn and Sample BRDFs
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Liu, Chen; Fischer, Michael; Ritschel, Tobias; Myszkowski, Karol; Niessner, Matthias
We propose a method to accelerate the joint process of physically acquiring and learning neural Bi-directional Reflectance Distribution Function (BRDF) models. While BRDF learning alone can be accelerated by meta-learning, acquisition remains slow as it relies on a mechanical process. We show that meta-learning can be extended to optimize the physical sampling pattern, too. After our method has been meta-trained for a set of fully-sampled BRDFs, it is able to quickly train on new BRDFs with up to five orders of magnitude fewer physical acquisition samples at similar quality. Our approach also extends to other linear and non-linear BRDF models, which we show in an extensive evaluation.
One Step Further Beyond Trilinear Interpolation and Central Differences: Triquadratic Reconstruction and its Analytic Derivatives at the Cost of One Additional Texture Fetch
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Csébfalvi, Balázs; Myszkowski, Karol; Niessner, Matthias
Recently, it has been shown that the quality of GPU-based trilinear volume resampling can be significantly improved if the six additional trilinear samples evaluated for the gradient estimation also contribute to the reconstruction of the underlying function [Csé19]. Although this improvement increases the approximation order from two to three without any extra cost, the continuity order remains C0. In this paper, we go one step further showing that a C1 continuous triquadratic B-spline reconstruction and its analytic partial derivatives can be evaluated by taking only one more trilinear sample into account. Thus, our method is the first volume-resampling technique that is nearly as fast as trilinear interpolation combined with on-thefly central differencing, but provides a higher-quality reconstruction together with a consistent analytic gradient calculation. Furthermore, we show that our fast evaluation scheme can also be adapted to the Mitchell-Netravali [MN88] notch filter, for which a fast GPU implementation has not been known so far.
Video Frame Interpolation for High Dynamic Range Sequences Captured with Dual-exposure Sensors
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Cogalan, Ugur; Bemana, Mojtaba; Seidel, Hans-Peter; Myszkowski, Karol; Myszkowski, Karol; Niessner, Matthias
Video frame interpolation (VFI) enables many important applications such as slow motion playback and frame rate conversion. However, one major challenge in using VFI is accurately handling high dynamic range (HDR) scenes with complex motion. To this end, we explore the possible advantages of dual-exposure sensors that readily provide sharp short and blurry long exposures that are spatially registered and whose ends are temporally aligned. This way, motion blur registers temporally continuous information on the scene motion that, combined with the sharp reference, enables more precise motion sampling within a single camera shot. We demonstrate that this facilitates a more complex motion reconstruction in the VFI task, as well as HDR frame reconstruction that so far has been considered only for the originally captured frames, not in-between interpolated frames. We design a neural network trained in these tasks that clearly outperforms existing solutions. We also propose a metric for scene motion complexity that provides important insights into the performance of VFI methods at test time.
Online Avatar Motion Adaptation to Morphologically-similar Spaces
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Choi, Soojin; Hong, Seokpyo; Cho, Kyungmin; Kim, Chaelin; Noh, Junyong; Myszkowski, Karol; Niessner, Matthias
In avatar-mediated telepresence systems, a similar environment is assumed for involved spaces, so that the avatar in a remote space can imitate the user's motion with proper semantic intention performed in a local space. For example, touching on the desk by the user should be reproduced by the avatar in the remote space to correctly convey the intended meaning. It is unlikely, however, that the two involved physical spaces are exactly the same in terms of the size of the room or the locations of the placed objects. Therefore, a naive mapping of the user's joint motion to the avatar will not create the semantically correct motion of the avatar in relation to the remote environment. Existing studies have addressed the problem of retargeting human motions to an avatar for telepresence applications. Few studies, however, have focused on retargeting continuous full-body motions such as locomotion and object interaction motions in a unified manner. In this paper, we propose a novel motion adaptation method that allows to generate the full-body motions of a human-like avatar on-the-fly in the remote space. The proposed method handles locomotion and object interaction motions as well as smooth transitions between them according to given user actions under the condition of a bijective environment mapping between morphologically-similar spaces. Our experiments show the effectiveness of the proposed method in generating plausible and semantically correct full-body motions of an avatar in room-scale space.
Parallel Transformation of Bounding Volume Hierarchies into Oriented Bounding Box Trees
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Vitsas, Nick; Evangelou, Iordanis; Papaioannou, Georgios; Gkaravelis, Anastasios; Myszkowski, Karol; Niessner, Matthias
Oriented bounding box (OBB) hierarchies can be used instead of hierarchies based on axis-aligned bounding boxes (AABB), providing tighter fitting to the underlying geometric structures and resulting in improved interference tests, such as ray-geometry intersections. In this paper, we present a method for the fast, parallel transformation of an existing bounding volume hierarchy (BVH), based on AABBs, into a hierarchy based on oriented bounding boxes. To this end, we parallelise a high-quality OBB extraction algorithm from the literature to operate as a standalone OBB estimator and further extend it to efficiently build an OBB hierarchy in a bottom up manner. This agglomerative approach allows for fast parallel execution and the formation of arbitrary, high-quality OBBs in bounding volume hierarchies. The method is fully implemented on the GPU and extensively evaluated with ray intersections.
IMoS: Intent-Driven Full-Body Motion Synthesis for Human-Object Interactions
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Ghosh, Anindita; Dabral, Rishabh; Golyanik, Vladislav; Theobalt, Christian; Slusallek, Philipp; Myszkowski, Karol; Niessner, Matthias
Can we make virtual characters in a scene interact with their surrounding objects through simple instructions? Is it possible to synthesize such motion plausibly with a diverse set of objects and instructions? Inspired by these questions, we present the first framework to synthesize the full-body motion of virtual human characters performing specified actions with 3D objects placed within their reach. Our system takes textual instructions specifying the objects and the associated 'intentions' of the virtual characters as input and outputs diverse sequences of full-body motions. This contrasts existing works, where full-body action synthesis methods generally do not consider object interactions, and human-object interaction methods focus mainly on synthesizing hand or finger movements for grasping objects. We accomplish our objective by designing an intent-driven fullbody motion generator, which uses a pair of decoupled conditional variational auto-regressors to learn the motion of the body parts in an autoregressive manner. We also optimize the 6-DoF pose of the objects such that they plausibly fit within the hands of the synthesized characters. We compare our proposed method with the existing methods of motion synthesis and establish a new and stronger state-of-the-art for the task of intent-driven motion synthesis.
Makeup Extraction of 3D Representation via Illumination-Aware Image Decomposition
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Yang, Xingchao; Taketomi, Takafumi; Kanamori, Yoshihiro; Myszkowski, Karol; Niessner, Matthias
Facial makeup enriches the beauty of not only real humans but also virtual characters; therefore, makeup for 3D facial models is highly in demand in productions. However, painting directly on 3D faces and capturing real-world makeup are costly, and extracting makeup from 2D images often struggles with shading effects and occlusions. This paper presents the first method for extracting makeup for 3D facial models from a single makeup portrait. Our method consists of the following three steps. First, we exploit the strong prior of 3D morphable models via regression-based inverse rendering to extract coarse materials such as geometry and diffuse/specular albedos that are represented in the UV space. Second, we refine the coarse materials, which may have missing pixels due to occlusions. We apply inpainting and optimization. Finally, we extract the bare skin, makeup, and an alpha matte from the diffuse albedo. Our method offers various applications for not only 3D facial models but also 2D portrait images. The extracted makeup is well-aligned in the UV space, from which we build a large-scale makeup dataset and a parametric makeup model for 3D faces. Our disentangled materials also yield robust makeup transfer and illumination-aware makeup interpolation/removal without a reference image.
Non-linear Rough 2D Animation using Transient Embeddings
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Even, Melvin; Bénard, Pierre; Barla, Pascal; Myszkowski, Karol; Niessner, Matthias
Traditional 2D animation requires time and dedication since tens of thousands of frames need to be drawn by hand for a typical production. Many computer-assisted methods have been proposed to automatize the generation of inbetween frames from a set of clean line drawings, but they are all limited by a rigid workflow and a lack of artistic controls, which is in the most part due to the one-to-one stroke matching and interpolation problems they attempt to solve. In this work, we take a novel view on those problems by focusing on an earlier phase of the animation process that uses rough drawings (i.e., sketches). Our key idea is to recast the matching and interpolation problems so that they apply to transient embeddings, which are groups of strokes that only exist for a few keyframes. A transient embedding carries strokes between keyframes both forward and backward in time through a sequence of transformed lattices. Forward and backward strokes are then cross-faded using their thickness to yield rough inbetweens. With our approach, complex topological changes may be introduced while preserving visual motion continuity. As demonstrated on state-of-the-art 2D animation exercises, our system provides unprecedented artistic control through the non-linear exploration of movements and dynamics in real-time.
Interactive Design of 2D Car Profiles with Aerodynamic Feedback
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Rosset, Nicolas; Cordonnier, Guillaume; Duvigneau, Régis; Bousseau, Adrien; Myszkowski, Karol; Niessner, Matthias
The design of car shapes requires a delicate balance between aesthetic and performance. While fluid simulation provides the means to evaluate the aerodynamic performance of a given shape, its computational cost hinders its usage during the early explorative phases of design, when aesthetic is decided upon. We present an interactive system to assist designers in creating aerodynamic car profiles. Our system relies on a neural surrogate model to predict fluid flow around car shapes, providing fluid visualization and shape optimization feedback to designers as soon as they sketch a car profile. Compared to prior work that focused on time-averaged fluid flows, we describe how to train our model on instantaneous, synchronized observations extracted from multiple pre-computed simulations, such that we can visualize and optimize for dynamic flow features, such as vortices. Furthermore, we architectured our model to support gradient-based shape optimization within a learned latent space of car profiles. In addition to regularizing the optimization process, this latent space and an associated encoder-decoder allows us to input and output car profiles in a bitmap form, without any explicit parameterization of the car boundary. Finally, we designed our model to support pointwise queries of fluid properties around car shapes, allowing us to adapt computational cost to application needs. As an illustration, we only query our model along streamlines for flow visualization, we query it in the vicinity of the car for drag optimization, and we query it behind the car for vortex attenuation.
What's in a Decade? Transforming Faces Through Time
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Chen, Eric Ming; Sun, Jin; Khandelwal, Apoorv; Lischinski, Dani; Snavely, Noah; Averbuch-Elor, Hadar; Myszkowski, Karol; Niessner, Matthias
How can one visually characterize photographs of people over time? In this work, we describe the Faces Through Time dataset, which contains over a thousand portrait images per decade from the 1880s to the present day. Using our new dataset, we devise a framework for resynthesizing portrait images across time, imagining how a portrait taken during a particular decade might have looked like had it been taken in other decades. Our framework optimizes a family of per-decade generators that reveal subtle changes that differentiate decades-such as different hairstyles or makeup-while maintaining the identity of the input portrait. Experiments show that our method can more effectively resynthesizing portraits across time compared to state-of-theart image-to-image translation methods, as well as attribute-based and language-guided portrait editing models. Our code and data will be available at facesthroughtime.github.io.
Scene-Aware 3D Multi-Human Motion Capture from a Single Camera
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Luvizon, Diogo C.; Habermann, Marc; Golyanik, Vladislav; Kortylewski, Adam; Theobalt, Christian; Myszkowski, Karol; Niessner, Matthias
In this work, we consider the problem of estimating the 3D position of multiple humans in a scene as well as their body shape and articulation from a single RGB video recorded with a static camera. In contrast to expensive marker-based or multi-view systems, our lightweight setup is ideal for private users as it enables an affordable 3D motion capture that is easy to install and does not require expert knowledge. To deal with this challenging setting, we leverage recent advances in computer vision using large-scale pre-trained models for a variety of modalities, including 2D body joints, joint angles, normalized disparity maps, and human segmentation masks. Thus, we introduce the first non-linear optimization-based approach that jointly solves for the 3D position of each human, their articulated pose, their individual shapes as well as the scale of the scene. In particular, we estimate the scene depth and person scale from normalized disparity predictions using the 2D body joints and joint angles. Given the per-frame scene depth, we reconstruct a point-cloud of the static scene in 3D space. Finally, given the per-frame 3D estimates of the humans and scene point-cloud, we perform a space-time coherent optimization over the video to ensure temporal, spatial and physical plausibility. We evaluate our method on established multi-person 3D human pose benchmarks where we consistently outperform previous methods and we qualitatively demonstrate that our method is robust to in-thewild conditions including challenging scenes with people of different sizes. Code: https://github.com/dluvizon/ scene-aware-3d-multi-human
Editing Compressed High-resolution Voxel Scenes with Attributes
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Molenaar, Mathijs; Eisemann, Elmar; Myszkowski, Karol; Niessner, Matthias
Sparse Voxel Directed Acyclic Graphs (SVDAGs) are an efficient solution for storing high-resolution voxel geometry. Recently, algorithms for the interactive modification of SVDAGs have been proposed that maintain the compressed geometric representation. Nevertheless, voxel attributes, such as colours, require an uncompressed storage, which can result in high memory usage over the course of the application. The reason is the high cost of existing attribute-compression schemes which remain unfit for interactive applications. In this paper, we introduce two attribute compression methods (lossless and lossy), which enable the interactive editing of compressed high-resolution voxel scenes including attributes.
Evolving Guide Subdivision
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Karciauskas, Kestutis; Peters, Jorg; Myszkowski, Karol; Niessner, Matthias
To overcome the well-known shape deficiencies of bi-cubic subdivision surfaces, Evolving Guide subdivision (EG subdivision) generalizes C2 bi-quartic (bi-4) splines that approximate a sequence of piecewise polynomial surface pieces near extraordinary points. Unlike guided subdivision, which achieves good shape by following a guide surface in a two-stage, geometry-dependent process, EG subdivision is defined by five new explicit subdivision rules. While formally only C1 at extraordinary points, EG subdivision applied to an obstacle course of inputs generates surfaces without the oscillations and pinched highlight lines typical for Catmull-Clark subdivision. EG subdivision surfaces join C2 with bi-3 surface pieces obtained by interpreting regular sub-nets as bi-cubic tensor-product splines and C2 with adjacent EG surfaces. The EG subdivision control net surrounding an extraordinary node can have the same structure as Catmull-Clark subdivision: two rings of 4-sided facets around each extraordinary nodes so that extraordinary nodes are separated by at least one regular node.
Physics-Informed Neural Corrector for Deformation-based Fluid Control
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Tang, Jingwei; Kim, Byungsoo; Azevedo, Vinicius C.; Solenthaler, Barbara; Myszkowski, Karol; Niessner, Matthias
Controlling fluid simulations is notoriously difficult due to its high computational cost and the fact that user control inputs can cause unphysical motion. We present an interactive method for deformation-based fluid control. Our method aims at balancing the direct deformations of fluid fields and the preservation of physical characteristics. We train convolutional neural networks with physics-inspired loss functions together with a differentiable fluid simulator, and provide an efficient workflow for flow manipulations at test time. We demonstrate diverse test cases to analyze our carefully designed objectives and show that they lead to physical and eventually visually appealing modifications on edited fluid data.
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Rodriguez-Pardo, Carlos; Prieto-Martín, Melania; Casas, Dan; Garces, Elena; Myszkowski, Karol; Niessner, Matthias
We propose a method to estimate the mechanical parameters of fabrics using a casual capture setup with a depth camera. Our approach enables to create mechanically-correct digital representations of real-world textile materials, which is a fundamental step for many interactive design and engineering applications. As opposed to existing capture methods, which typically require expensive setups, video sequences, or manual intervention, our solution can capture at scale, is agnostic to the optical appearance of the textile, and facilitates fabric arrangement by non-expert operators. To this end, we propose a sim-to-real strategy to train a learning-based framework that can take as input one or multiple images and outputs a full set of mechanical parameters. Thanks to carefully designed data augmentation and transfer learning protocols, our solution generalizes to real images despite being trained only on synthetic data, hence successfully closing the sim-to-real loop. Key in our work is to demonstrate that evaluating the regression accuracy based on the similarity at parameter space leads to an inaccurate distances that do not match the human perception. To overcome this, we propose a novel metric for fabric drape similarity that operates on the image domain instead on the parameter space, allowing us to evaluate our estimation within the context of a similarity rank. We show that out metric correlates with human judgments about the perception of drape similarity, and that our model predictions produce perceptually accurate results compared to the ground truth parameters.
Img2Logo: Generating Golden Ratio Logos from Images
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Hsiao, Kai-Wen; Yang, Yong-Liang; Chiu, Yung-Chih; Hu, Min-Chun; Yao, Chih-Yuan; Chu, Hung-Kuo; Myszkowski, Karol; Niessner, Matthias
Logos are one of the most important graphic design forms that use an abstracted shape to clearly represent the spirit of a community. Among various styles of abstraction, a particular golden-ratio design is frequently employed by designers to create a concise and regular logo. In this context, designers utilize a set of circular arcs with golden ratios (i.e., all arcs are taken from circles whose radii form a geometric series based on the golden ratio) as the design elements to manually approximate a target shape. This error-prone process requires a large amount of time and effort, posing a significant challenge for design space exploration. In this work, we present a novel computational framework that can automatically generate golden ratio logo abstractions from an input image. Our framework is based on a set of carefully identified design principles and a constrained optimization formulation respecting these principles. We also propose a progressive approach that can efficiently solve the optimization problem, resulting in a sequence of abstractions that approximate the input at decreasing levels of detail. We evaluate our work by testing on images with different formats including real photos, clip arts, and line drawings. We also extensively validate the key components and compare our results with manual results by designers to demonstrate the effectiveness of our framework. Moreover, our framework can largely benefit design space exploration via easy specification of design parameters such as abstraction levels, golden circle sizes, etc.
Directionality-Aware Design of Embroidery Patterns
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Zhenyuan, Liu; Piovarci, Michal; Hafner, Christian; Charrondière, Raphaël; Bickel, Bernd; Myszkowski, Karol; Niessner, Matthias
Embroidery is a long-standing and high-quality approach to making logos and images on textiles. Nowadays, it can also be performed via automated machines that weave threads with high spatial accuracy. A characteristic feature of the appearance of the threads is a high degree of anisotropy. The anisotropic behavior is caused by depositing thin but long strings of thread. As a result, the stitched patterns convey both color and direction. Artists leverage this anisotropic behavior to enhance pure color images with textures, illusions of motion, or depth cues. However, designing colorful embroidery patterns with prescribed directionality is a challenging task, one usually requiring an expert designer. In this work, we propose an interactive algorithm that generates machine-fabricable embroidery patterns from multi-chromatic images equipped with user-specified directionality fields.We cast the problem of finding a stitching pattern into vector theory. To find a suitable stitching pattern, we extract sources and sinks from the divergence field of the vector field extracted from the input and use them to trace streamlines. We further optimize the streamlines to guarantee a smooth and connected stitching pattern. The generated patterns approximate the color distribution constrained by the directionality field. To allow for further artistic control, the trade-off between color match and directionality match can be interactively explored via an intuitive slider. We showcase our approach by fabricating several embroidery paths.
Surface Maps via Adaptive Triangulations
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Schmidt, Patrick; Pieper, Dörte; Kobbelt, Leif; Myszkowski, Karol; Niessner, Matthias
We present a new method to compute continuous and bijective maps (surface homeomorphisms) between two or more genus-0 triangle meshes. In contrast to previous approaches, we decouple the resolution at which a map is represented from the resolution of the input meshes. We discretize maps via common triangulations that approximate the input meshes while remaining in bijective correspondence to them. Both the geometry and the connectivity of these triangulations are optimized with respect to a single objective function that simultaneously controls mapping distortion, triangulation quality, and approximation error. A discrete-continuous optimization algorithm performs both energy-based remeshing as well as global second-order optimization of vertex positions, parametrized via the sphere. With this, we combine the disciplines of compatible remeshing and surface map optimization in a unified formulation and make a contribution in both fields. While existing compatible remeshing algorithms often operate on a fixed pre-computed surface map, we can now globally update this correspondence during remeshing. On the other hand, bijective surface-to-surface map optimization previously required computing costly overlay meshes that are inherently tied to the input mesh resolution. We achieve significant complexity reduction by instead assessing distortion between the approximating triangulations. This new map representation is inherently more robust than previous overlay-based approaches, is less intricate to implement, and naturally supports mapping between more than two surfaces. Moreover, it enables adaptive multi-resolution schemes that, e.g., first align corresponding surface regions at coarse resolutions before refining the map where needed. We demonstrate significant speedups and increased flexibility over state-of-the art mapping algorithms at similar map quality, and also provide a reference implementation of the method.
Scalable and Efficient Functional Map Computations on Dense Meshes
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Magnet, Robin; Ovsjanikov, Maks; Myszkowski, Karol; Niessner, Matthias
We propose a new scalable version of the functional map pipeline that allows to efficiently compute correspondences between potentially very dense meshes. Unlike existing approaches that process dense meshes by relying on ad-hoc mesh simplification, we establish an integrated end-to-end pipeline with theoretical approximation analysis. In particular, our method overcomes the computational burden of both computing the basis, as well the functional and pointwise correspondence computation by approximating the functional spaces and the functional map itself. Errors in the approximations are controlled by theoretical upper bounds assessing the range of applicability of our pipeline.With this construction in hand, we propose a scalable practical algorithm and demonstrate results on dense meshes, which approximate those obtained by standard functional map algorithms at the fraction of the computation time. Moreover, our approach outperforms the standard acceleration procedures by a large margin, leading to accurate results even in challenging cases.

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