EG2024

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https://diglib.eg.org/handle/10.2312/3606947

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ShapeVerse: Physics-based Characters with Varied Body Shapes
(The Eurographics Association, 2024) Vyas, Bharat; O'Sullivan, Carol; Liu, Lingjie; Averkiou, Melinos
Computer animation of realistic human characters remains a significant challenge. This work used deep reinforcement learning to generate physics-based characters with diverse body shapes. We aimed to replicate reference motions like walking or jogging while considering individual variations in body shape and mass. Reference motions served as training targets, accounting for differences in shape parameters to accommodate mass variations. This method produced animations that accurately capture human motion details, leading to diverse and lifelike character performances.
Neural Moment Transparency
(The Eurographics Association, 2024) Tsopouridis, Grigoris; Vasilakis, Andreas Alexandros; Fudos, Ioannis; Hu, Ruizhen; Charalambous, Panayiotis
We have developed a machine learning approach to efficiently compute per-fragment transmittance, using transmittance composed and accumulated with moment statistics, on a fragment shader. Our approach excels in achieving superior visual accuracy for computing order-independent transparency (OIT) in scenes with high depth complexity when compared to prior art.
From Few to Full: High-Resolution 3D Object Reconstruction from Sparse Views and Unknown Poses
(The Eurographics Association, 2024) Yao, Grekou; Mavromatis, Sebastien; Mari, Jean-Luc; Liu, Lingjie; Averkiou, Melinos
Recent progress in 3D reconstruction has been driven by generative models, moving from traditional multi-view dependence to single-image diffusion model based techniques. However, these innovative approaches often face challenges with sparse view scenarios, requiring known poses or template shapes, often failing in high-resolution reconstructions. Addressing these issues, we introduce the ''F2F'' (Few to Full) framework, designed for crafting high-resolution 3D models from few views and unknown camera poses, creating fully realistic 3D objects without external constraints. F2F employs a hybrid approach, optimizing both implicit and explicit representations through a unique pipeline involving a pretrained diffusion model for pose estimation, a deformable tetrahedra grid for feature volume construction, and an MLP (neural network) for surface optimization. Our method sets a new standard by ensuring surface geometry, topology, and semantic consistency through differentiable rendering, aiming for a comprehensive solution in 3D reconstruction from sparse views.
Interactive VPL-based Global Illumination on the GPU Using Adaptive Fuzzy Clustering
(The Eurographics Association, 2024) Colom, Arnau; Marques, Ricardo; Santos, Luís Paulo; Liu, Lingjie; Averkiou, Melinos
Physically-based synthesis of high quality imagery results in a significant workload, which makes interactive rendering a very challenging task. Our approach to achieve such interactive frame rates while accurately simulating global illumination phenomena entails developing a Virtual Point Lights (VPL) ray tracer that runs entirely in the GPU. Our performance guarantees arise from clustering both shading points and VPLs and computing visibility only among clusters' representatives. Previous approaches to the same problem resort to K-means clustering, which requires the user to specify the number of clusters; a rather unintuitive requirement. We propose an innovative massively parallel, GPU-efficient, Quality-Threshold clustering algorithm, which requires the user to specify a quality parameter. The algorithm dynamically adjusts the number of clusters depending both on the specified quality threshold and on camera-geometry conditions during execution.
Predictive Modeling of Material Appearance: From the Drawing Board to Interdisciplinary Applications
(The Eurographics Association, 2024) Baranoski, Gladimir V. G.; Mania, Katerina; Artusi, Alessandro
This tutorial addresses one of the fundamental and timely topics of computer graphics research, namely the predictive modeling of material appearance. Although this topic is deeply rooted in traditional areas like rendering and natural phenomena simulation, this tutorial is not limited to cover contents connected to these areas. It also closely looks into the scientific methodology employed in the development of predictive models of light and matter interactions. Given the widespread use of this methodology to find modeling solutions for problems within and outside computer graphics, its discussion from a ''behind the scenes'' perspective aims to underscore practical and far-reaching aspects of interdisciplinary research that are often overlooked in related publications. More specifically, this tutorial unveils constraints and pitfalls found in each of the key stages of the model development process, namely data collection, design and evaluation, and brings forward alternatives to tackle them effectively. Furthermore, besides being a central component of realistic image synthesis frameworks, predictive material appearance models have a scope of applications that can be extended far beyond the generation of believable images. For instance, they can be employed to accelerate the hypothesis generation and validation cycles of research across a wide range of fields, from biology and medicine to photonics and remote sensing, among others. These models can also be used to generate comprehensive in silico (computational) datasets to support the translation of knowledge advances in those fields to real-world applications (e.g., the noninvasive screening of medical conditions and the remote detection of environmental hazards). In fact, a number of them are already being used in physical and life sciences, notably to support investigations seeking to strengthen the current understanding about material appearance changes prompted by mechanisms which cannot be fully studied using standard ''wet'' experimental procedures. Accordingly, such interdisciplinary research initiatives are also discussed in this tutorial through selected case studies involving the use of predictive material appearance models to elucidate challenging scientific questions.
The Use of Photogrammetry in Historic Preservation Curriculum: A Comparative Case Study
(The Eurographics Association, 2024) Kepczynska-Walczak, Anetta; Walczak, Bartosz M.; Zarzycki, Andrzej; Sousa Santos, Beatriz; Anderson, Eike
Computer graphic techniques have emerged as a key player in digital heritage preservation and its dissemination. Photogrammetry allows for high-fidelity captures and virtual reconstructions of the built environment that can be further ported into virtual reality (VR) and augmented reality (AR) experiences. This paper provides a comparative analysis of historic details and building documentation methods in heritage preservation in the context of architectural education. Specifically, it compares two educational case studies conducted in 10-year intervals documenting the same set of historic artifacts with corresponding state-of-the-art digital technologies. The methodology for this paper is a qualitative comparative analysis of two surveying projects that utilized distinct emerging digital technology while sharing the same study subjects and similar tool-driven curricular framework. The research also incorporates a student survey, offering perspectives on teaching strategies and outcomes within this dynamic educational context. The outcomes demonstrate that the technological (tool-driven) shift impacts the way students interact with the investigated artifacts and the changing role of the interpretative versus analytical skills needed to delineate the work. It also changes what are considered primary and secondary knowledge sources.
Real-time Seamless Object Space Shading
(The Eurographics Association, 2024) Li, Tianyu; Guo, Xiaoxin; Hu, Ruizhen; Charalambous, Panayiotis
Object space shading remains a challenging problem in real-time rendering due to runtime overhead and object parameterization limitations. While the recently developed algorithm by Baker et al. [BJ22] enables high-performance real-time object space shading, it still suffers from seam artifacts. In this paper, we introduce an innovative object space shading system leveraging a virtualized per-halfedge texturing schema to obviate excessive shading and preclude texture seam artifacts. Moreover, we implement ReSTIR GI on our system (see Figure 1), removing the necessity of temporally reprojecting shading samples and improving the convergence of areas of disocclusion. Our system yields superior results in terms of both efficiency and visual fidelity.
Tackling Diverse Student Backgrounds and Goals while Teaching an Introductory Visual Computing Course at M.Sc. Level
(The Eurographics Association, 2024) Silva, Samuel; Sousa Santos, Beatriz; Anderson, Eike
Visual Computing entails a set of competences that are core for those pursuing Digital Game Development and has become a much sought competence for professionals in a wide variety of fields. In the particular case presented here, the course serves a diverse audience from Multimedia and Design students without previous knowledge in the field and low programming competences, to students that have a previous BS.c in Game Development and have already covered the basic concepts in a previous course. Additionally, the course is also offered as an elective for Computer Science M.Sc. students. This diverse set of background competences and goals motivated designing an approach to the course where each student can build on previous knowledge and have a say on its personal learning path. This article shares the overall approach, presents and discusses the outcomes, and reflects on future evolutions.
Text-to-3D Shape Generation
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lee, Hanhung; Savva, Manolis; Chang, Angel Xuan; Aristidou, Andreas; Macdonnell, Rachel
Recent years have seen an explosion of work and interest in text-to-3D shape generation. Much of the progress is driven by advances in 3D representations, large-scale pretraining and representation learning for text and image data enabling generative AI models, and differentiable rendering. Computational systems that can perform text-to-3D shape generation have captivated the popular imagination as they enable non-expert users to easily create 3D content directly from text. However, there are still many limitations and challenges remaining in this problem space. In this state-of-the-art report, we provide a survey of the underlying technology and methods enabling text-to-3D shape generation to summarize the background literature. We then derive a systematic categorization of recent work on text-to-3D shape generation based on the type of supervision data required. Finally, we discuss limitations of the existing categories of methods, and delineate promising directions for future work.
Fast Dynamic Facial Wrinkles
(The Eurographics Association, 2024) Weiss, Sebastian; Chandran, Prashanth; Zoss, Gaspard; Bradley, Derek; Hu, Ruizhen; Charalambous, Panayiotis
We present a new method to animate the dynamic motion of skin micro wrinkles under facial expression deformation. Since wrinkles are formed as a reservoir of skin for stretching, our model only deforms wrinkles that are perpendicular to the stress axis. Specifically, those wrinkles become wider and shallower when stretched, and deeper and narrower when compressed. In contrast to previous methods that attempted to modify the neutral wrinkle displacement map, our approach is to modify the way wrinkles are constructed in the displacement map. To this end, we build upon a previous synthetic wrinkle generator that allows us to control the width and depth of individual wrinkles when generated on a per-frame basis. Furthermore, since constructing a displacement map per frame of animation is costly, we present a fast approximation approach using pre-computed displacement maps of wrinkles binned by stretch direction, which can be blended interactively in a shader. We compare both our high quality and fast methods with previous techniques for wrinkle animation and demonstrate that our work retains more realistic details.
DeepIron: Predicting Unwarped Garment Texture from a Single Image
(The Eurographics Association, 2024) Kwon, Hyun-Song; Lee, Sung-Hee; Hu, Ruizhen; Charalambous, Panayiotis
Realistic reconstruction of 3D clothing from an image has wide applications, such as avatar creation and virtual try-on. This paper presents a novel framework that reconstructs the texture map for 3D garments from a single garment image with pose. Since 3D garments are effectively modeled by stitching 2D garment sewing patterns, our specific goal is to generate a texture image for the sewing patterns. A key component of our framework, the Texture Unwarper, infers the original texture image from the input garment image, which exhibits warping and occlusion of the garment due to the user's body shape and pose. This is effectively achieved by translating between the input and output images by mapping the latent spaces of the two images. By inferring the unwarped original texture of the input garment, our method helps reconstruct 3D garment models that can show high-quality texture images realistically deformed for new poses. We validate the effectiveness of our approach through a comparison with other methods and ablation studies.
Modern Dance Retargeting using Ribbons as Lines of Action
(The Eurographics Association, 2024) Vialle, Manon; Ronfard, Rémi; Skouras, Melina; Hu, Ruizhen; Charalambous, Panayiotis
We present a method for retargetting dancing characters represented as articulated skeletons with possibly different morphologies and topologies. Our approach relies on the use of flexible ribbons that can bend and twist as an intermediate representation, and that can be seen as animated lines of action. These ribbons allow us to abstract away the specific morphology of the bodies and to well transmit the fluidity of modern dance movement from one character to another.
EUROGRAPHICS 2024: Short Papers Frontmatter
(Eurographics Association, 2024) Hu, Ruizhen; Charalambous, Panayiotis; Hu, Ruizhen; Charalambous, Panayiotis
State of the Art on Diffusion Models for Visual Computing
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Po, Ryan; Yifan, Wang; Golyanik, Vladislav; Aberman, Kfir; Barron, Jon T.; Bermano, Amit; Chan, Eric; Dekel, Tali; Holynski, Aleksander; Kanazawa, Angjoo; Liu, C. Karen; Liu, Lingjie; Mildenhall, Ben; Nießner, Matthias; Ommer, Björn; Theobalt, Christian; Wonka, Peter; Wetzstein, Gordon; Aristidou, Andreas; Macdonnell, Rachel
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes. In these domains, diffusion models are the generative AI architecture of choice. Within the last year alone, the literature on diffusion-based tools and applications has seen exponential growth and relevant papers are published across the computer graphics, computer vision, and AI communities with new works appearing daily on arXiv. This rapid growth of the field makes it difficult to keep up with all recent developments. The goal of this state-of-the-art report (STAR) is to introduce the basic mathematical concepts of diffusion models, implementation details and design choices of the popular Stable Diffusion model, as well as overview important aspects of these generative AI tools, including personalization, conditioning, inversion, among others. Moreover, we give a comprehensive overview of the rapidly growing literature on diffusion-based generation and editing, categorized by the type of generated medium, including 2D images, videos, 3D objects, locomotion, and 4D scenes. Finally, we discuss available datasets, metrics, open challenges, and social implications. This STAR provides an intuitive starting point to explore this exciting topic for researchers, artists, and practitioners alike.
Gaming to Learn: A Pilot Case Study on Students Acceptance of Playing Video Games as a Learning Method
(The Eurographics Association, 2024) Nisiotis, Louis; Sousa Santos, Beatriz; Anderson, Eike
This paper presents a case study on playing video games as a method to support the delivery of a game development University module, describing the teaching methodology and presenting details on a 'gaming' for learning approach to support the module's learning objectives. It presents the formulation of a theoretical framework to evaluate students acceptance of playing video games as a learning method, and the results of a pilot study using a modified Technology Acceptance Model. The results revealed that gaming as a learning activity was positively perceived by students, finding this method engaging and relevant to their learning curriculum, playful, enjoyable, useful, easy to use, with positive attitudes and behavioural intentions to use. This pilot case study serves as a practical example of implementing video games to support learning, preparing the methodology for further research to understand students acceptance, and the effect on learning outcomes and knowledge acquisition.
EUROGRAPHICS 2024: Posters Frontmatter
(Eurographics Association, 2024) Liu, Lingjie; Averkiou, Melinos; Liu, Lingjie; Averkiou, Melinos
Emotional Responses to Exclusionary Behaviors in Intelligent Embodied Augmented Reality Agents
(The Eurographics Association, 2024) Apostolou, Kalliopi; Milata, Vaclav; Škola, Filip; Liarokapis, Fotis; Hu, Ruizhen; Charalambous, Panayiotis
This study investigated how interactions with intelligent agents, embodied as augmented reality (AR) avatars displaying exclusionary behaviors, affect users' emotions. Six participants engaged using voice interaction in a knowledge acquisition scenario in an AR environment with two ChatGPT-driven agents. The gaze-aware avatars, simulating realistic body language, progressively demonstrated social exclusion behaviors. Although not statistically significant, our data suggest a post-interaction emotional shift, manifested by decreased positive and negative affect-aligning with previous studies on social exclusion. Qualitative feedback revealed that some users attributed the exclusionary behavior of avatars to system glitches, leading to their disengagement. Our findings highlight challenges and opportunities for embodied intelligent agents, underscoring their potential to shape user experiences within AR, and the broader extended reality (XR) landscape.
A Research Methodology Course in a Game Development Curriculum
(The Eurographics Association, 2024) Hu, Yan; Sundstedt, Veronica; Goswami, Prashant; Sousa Santos, Beatriz; Anderson, Eike
Research methodology courses can often be considered part of a computer science curriculum. These basic or advanced-level courses are taught in terms of traditional research methods. This paper presents and discusses a research methodology course curriculum for students studying programs focusing on digital game development (more specifically, focusing on game engineering). Our research methodology course prepares students for their upcoming thesis by encouraging a research-oriented approach. This is done by exploring new research areas in game engineering as a basis for research analysis and by applying research methods practically in a smaller project. This paper presents the course structure, assignments, and lessons learned. Together with existing literature, it demonstrates important aspects to consider in teaching and learning game research methodologies. The course evaluation found that the students appreciated the interactive lectures, close staff supervision, and detailed feedback on the scientific writing process.
Next Generation 3D Face Models
(The Eurographics Association, 2024) Chandran, Prashanth; Yang, Lingchen; Mania, Katerina; Artusi, Alessandro
Having a compact, expressive and artist friendly way to represent and manipulate human faces has been of prime interest to the visual effects community for the past several decades as face models play a very important role in many face capture workflows. In this short course, we go over the evolution of 3D face models used to model and animate facial identity and expression in the computer graphics community, and discuss how the recent emergence of deep face models is transforming this landscape by enabling new artistic choices. In this first installment, the course will take the audience through the evolution of face models, starting with simple blendshape models introduced in the 1980s; that continue to be extremely popular today, to recent deep shape models that utilize neural networks to represent and manipulate face shapes in an artist friendly fashion. As the course is meant to be beginner friendly, the course will commence with a quick introduction to non-neural parametric shape models starting with linear blendshape and morphable models. We will then switch focus to deep shape models, particularly those that offer intuitive control to artists. We will discuss multiple variants of such deep face models that i) allow semantic control, ii) are agnostic to the underlying topology of the manipulated shape, iii) provide the ability to explicitly model a sequence of 3D shapes or animations, and iv) allow for the simulation of physical effects. Applications that will be discussed include face shape synthesis, identity and expression interpolation, rig generation, performance retargeting, animation synthesis and more.
StarDEM: Efficient Discrete Element Method for Star-shaped Particles
(The Eurographics Association, 2024) Schreck, Camille; Lefebvre, Sylvain; Jourdan, David; Martínez, Jonàs; Hu, Ruizhen; Charalambous, Panayiotis
Granular materials composed of particles with complex shapes are challenging to simulate due to the high number of collisions between the particles. In this context, star shapes are promising: they cover a wide range of geometries from convex to concave and have interesting geometric properties. We propose an efficient method to simulate a large number of identical star-shaped particles. Our method relies on an effective approximation of the contacts between particles that can handle complex shapes, including highly non-convex ones. We demonstrate our method by implementing it in a 2D simulation using the Discrete Element Method, both on the CPU and GPU.

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