EG 2024 - STARs (CGF 43-2)

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    EUROGRAPHICS 2024: CGF 43-2 STARs Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Aristidou, Andreas; Macdonnell, Rachel; Aristidou, Andreas; Macdonnell, Rachel
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    Cues to fast-forward collaboration: A Survey of Workspace Awareness and Visual Cues in XR Collaborative Systems
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Assaf, Rodrigo; Mendes, Daniel; Rodrigues, Rui; Aristidou, Andreas; Macdonnell, Rachel
    Collaboration in extended reality (XR) environments presents complex challenges that revolve around how users perceive the presence, intentions, and actions of their collaborators. This paper delves into the intricate realm of group awareness, focusing specifically on workspace awareness and the innovative visual cues designed to enhance user comprehension. The research begins by identifying a spectrum of collaborative situations drawn from an analysis of XR prototypes in the existing literature. Then, we describe and introduce a novel classification for workspace awareness, along with an exploration of visual cues recently employed in research endeavors. Lastly, we present the key findings and shine a spotlight on promising yet unexplored topics. This work not only serves as a reference for experienced researchers seeking to inform the design of their own collaborative XR applications but also extends a welcoming hand to newcomers in this dynamic field.
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    A Survey on Cage-based Deformation of 3D Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Ströter, Daniel; Thiery, Jean-Marc; Hormann, Kai; Chen, Jiong; Chang, Qingjun; Besler, Sebastian; Mueller-Roemer, Johannes Sebastian; Boubekeur, Tamy; Stork, André; Fellner, Dieter W.; Aristidou, Andreas; Macdonnell, Rachel
    Interactive deformation via control handles is essential in computer graphics for the modeling of 3D geometry. Deformation control structures include lattices for free-form deformation and skeletons for character articulation, but this report focuses on cage-based deformation. Cages for deformation control are coarse polygonal meshes that encase the to-be-deformed geometry, enabling high-resolution deformation. Cage-based deformation enables users to quickly manipulate 3D geometry by deforming the cage. Due to their utility, cage-based deformation techniques increasingly appear in many geometry modeling applications. For this reason, the computer graphics community has invested a great deal of effort in the past decade and beyond into improving automatic cage generation and cage-based deformation. Recent advances have significantly extended the practical capabilities of cage-based deformation methods. As a result, there is a large body of research on cage-based deformation. In this report, we provide a comprehensive overview of the current state of the art in cage-based deformation of 3D geometry. We discuss current methods in terms of deformation quality, practicality, and precomputation demands. In addition, we highlight potential future research directions that overcome current issues and extend the set of practical applications. In conjunction with this survey, we publish an application to unify the most relevant deformation methods. Our report is intended for computer graphics researchers, developers of interactive geometry modeling applications, and 3D modeling and character animation artists.
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    Snow and Ice Animation Methods in Computer Graphics
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Goswami, Prashant; Aristidou, Andreas; Macdonnell, Rachel
    Snow and ice animation methods are becoming increasingly popular in the field of computer graphics (CG). The applications of snow and ice in CG are varied, ranging from generating realistic background landscapes to avalanches and physical interaction with objects in movies, games, etc. Over the past two decades, several methods have been proposed to capture the time-evolving physical appearance or simulation of snow and ice using different models at different scales. This state-of-the-art report aims to identify existing animation methods in the field, provide an up-to-date summary of the research in CG, and identify gaps for promising future work. Furthermore, we also attempt to identify the primarily related work done on snow and ice in some other disciplines, such as civil or mechanical engineering, and draw a parallel with the similarities and differences in CG.
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    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.
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    Recent Trends in 3D Reconstruction of General Non-Rigid Scenes
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Yunus, Raza; Lenssen, Jan Eric; Niemeyer, Michael; Liao, Yiyi; Rupprecht, Christian; Theobalt, Christian; Pons-Moll, Gerard; Huang, Jia-Bin; Golyanik, Vladislav; Ilg, Eddy; Aristidou, Andreas; Macdonnell, Rachel
    Reconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision. It enables the synthesizing of photorealistic novel views, useful for the movie industry and AR/VR applications. It also facilitates the content creation necessary in computer games and AR/VR by avoiding laborious manual design processes. Further, such models are fundamental for intelligent computing systems that need to interpret real-world scenes and actions to act and interact safely with the human world. Notably, the world surrounding us is dynamic, and reconstructing models of dynamic, non-rigidly moving scenes is a severely underconstrained and challenging problem. This state-of-the-art report (STAR) offers the reader a comprehensive summary of state-of-the-art techniques with monocular and multi-view inputs such as data from RGB and RGB-D sensors, among others, conveying an understanding of different approaches, their potential applications, and promising further research directions. The report covers 3D reconstruction of general non-rigid scenes and further addresses the techniques for scene decomposition, editing and controlling, and generalizable and generative modeling. More specifically, we first review the common and fundamental concepts necessary to understand and navigate the field and then discuss the state-of-the-art techniques by reviewing recent approaches that use traditional and machine-learning-based neural representations, including a discussion on the newly enabled applications. The STAR is concluded with a discussion of the remaining limitations and open challenges.
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    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.
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    Virtual Instrument Performances (VIP): A Comprehensive Review
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Kyriakou, Theodoros; Alvarez de la Campa Crespo, Merce; Panayiotou, Andreas; Chrysanthou, Yiorgos; Charalambous, Panayiotis; Aristidou, Andreas; Aristidou, Andreas; Macdonnell, Rachel
    Driven by recent advancements in Extended Reality (XR), the hype around the Metaverse, and real-time computer graphics, the transformation of the performing arts, particularly in digitizing and visualizing musical experiences, is an ever-evolving landscape. This transformation offers significant potential in promoting inclusivity, fostering creativity, and enabling live performances in diverse settings. However, despite its immense potential, the field of Virtual Instrument Performances (VIP) has remained relatively unexplored due to numerous challenges. These challenges arise from the complex and multi-modal nature of musical instrument performances, the need for high precision motion capture under occlusions including the intricate interactions between a musician's body and fingers with instruments, the precise synchronization and seamless integration of various sensory modalities, accommodating variations in musicians' playing styles, facial expressions, and addressing instrumentspecific nuances. This comprehensive survey delves into the intersection of technology, innovation, and artistic expression in the domain of virtual instrument performances. It explores musical performance multi-modal databases and investigates a wide range of data acquisition methods, encompassing diverse motion capture techniques, facial expression recording, and various approaches for capturing audio and MIDI data (Musical Instrument Digital Interface). The survey also explores Music Information Retrieval (MIR) tasks, with a particular emphasis on the Musical Performance Analysis (MPA) field, and offers an overview of various works in the realm of Musical Instrument Performance Synthesis (MIPS), encompassing recent advancements in generative models. The ultimate aim of this survey is to unveil the technological limitations, initiate a dialogue about the current challenges, and propose promising avenues for future research at the intersection of technology and the arts.
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    A Survey on Realistic Virtual Human Animations: Definitions, Features and Evaluations
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Rekik, Rim; Wuhrer, Stefanie; Hoyet, Ludovic; Zibrek, Katja; Olivier, Anne-Hélène; Aristidou, Andreas; Macdonnell, Rachel
    Generating realistic animated virtual humans is a problem that has been extensively studied with many applications in different types of virtual environments. However, the creation process of such realistic animations is challenging, especially because of the number and variety of influencing factors, that should then be identified and evaluated. In this paper, we attempt to provide a clearer understanding of how the multiple factors that have been studied in the literature impact the level of realism of animated virtual humans, by providing a survey of studies assessing their realism. This includes a review of features that have been manipulated to increase the realism of virtual humans, as well as evaluation approaches that have been developed. As the challenges of evaluating animated virtual humans in a way that agrees with human perception are still active research problems, this survey further identifies important open problems and directions for future research.