EG 2026 - STARs (CGF 45-2)

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/3607274
EG 2026 STARs
Table of Contents
State of the Art Reports
Advances in Neural 3D Mesh Texturing: A Survey
[ full paper Image] [ meta data Image]
Perla Sai Raj Kishore, Zhang Hao, Mahdavi-Amiri Ali
Survey on Differential Estimators for 3D Point Clouds
[ full paper Image] [ meta data Image]
Arnal-Anger Léo, Lejemble Thibault, Coeurjolly David, Barthe Loïc, Mellado Nicolas
State-of-the-Art in Deep Learning Approaches for Automatic Single-Panorama Indoor Modeling and Exploration
[ full paper Image] [ meta data Image]
Pintore Giovanni, Agus Marco, Schneider Jens, Gobbetti Enrico
Establishing Shape Correspondences: A Survey
[ full paper Image] [ meta data Image]
Heuschling Alexandra, Meinhold Hannah, Kobbelt Leif
How to Build Digital Humans? From Priors to Photorealistic Avatars
[ full paper Image] [ meta data Image]
Zielonka Wojciech, Kirschstein Tobias, Bolkart Timo, Giebenhain Simon, Sklyarova Vanessa, Deng Xiang, Xiang Donglai, Saito Shunsuke, Liu Yebin, Niessner Matthias, Thies Justus
Magnetic Modeling and Simulation for Computer Graphics
[ full paper Image] [ meta data Image]
Ni Xingyu, Zhu Yuechen, Wang Ruicheng, Wang Bin
Non-Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunities
[ full paper Image] [ meta data Image]
Zhuravlev Aleksei, Bastian Lennart, Cao Dongliang, El Amrani Nafie, Roetzer Paul, Ehm Viktoria, Marin Riccardo, Nishizawa Hiroki, Morishima Shigeo, Theobalt Christian, Navab Nassir, Cremers Daniel, Bernard Florian, Lähner Zorah, Golyanik Vladislav

BibTeX (EG 2026 - STARs (CGF 45-2))
                
@article{
10.1111:cgf.70392,

journal = {Computer Graphics Forum},
title = {{
Advances in Neural 3D Mesh Texturing: A Survey}},

author = {
Perla, Sai Raj Kishore
 and 
Zhang, Hao 
 and 
Mahdavi-Amiri, Ali 
},
year = {
2026},

publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},


ISSN = {1467-8659},

DOI = {
10.1111/cgf.70392}

}
                
@article{
10.1111:cgf.70395,

journal = {Computer Graphics Forum},
title = {{
How to Build Digital Humans? From Priors to Photorealistic Avatars}},

author = {
Zielonka, Wojciech 
 and 
Kirschstein, Tobias 
 and 
Thies, Justus 
 and 
Bolkart, Timo
 and 
Giebenhain, Simon 
 and 
Sklyarova, Vanessa 
 and 
Deng, Xiang 
 and 
Xiang, Donglai
 and 
Saito, Shunsuke
 and 
Liu, Yebin
 and 
Niessner, Matthias
},
year = {
2026},

publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},


ISSN = {1467-8659},

DOI = {
10.1111/cgf.70395}

}
                
@article{
10.1111:cgf.70396,

journal = {Computer Graphics Forum},
title = {{
State-of-the-art in deep learning approaches for automatic single-panorama indoor modeling and exploration}},

author = {
Pintore, Giovanni
 and 
Agus, Marco
 and 
Schneider, Jens
 and 
Gobbetti, Enrico
},
year = {
2026},

publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},


ISSN = {1467-8659},

DOI = {
10.1111/cgf.70396}

}
                
@article{
10.1111:cgf.70397,

journal = {Computer Graphics Forum},
title = {{
Non-Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunities}},

author = {
Zhuravlev, Aleksei
 and 
Bastian, Lennart
 and 
Navab, Nassir
 and 
Cremers, Daniel
 and 
Bernard, Florian
 and 
Lähner, Zorah
 and 
Golyanik, Vladislav
 and 
Cao, Dongliang
 and 
El Amrani, Nafie
 and 
Roetzer, Paul
 and 
Ehm, Viktoria
 and 
Marin, Riccardo
 and 
Nishizawa, Hiroki
 and 
Morishima, Shigeo
 and 
Theobalt, Christian
},
year = {
2026},

publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},


ISSN = {1467-8659},

DOI = {
10.1111/cgf.70397}

}
                
@article{
10.1111:cgf.70406,

journal = {Computer Graphics Forum},
title = {{
Establishing Shape Correspondences: A Survey}},

author = {
Heuschling, Alexandra
 and 
Meinhold, Hannah
 and 
Kobbelt, Leif
},
year = {
2026},

publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},


ISSN = {1467-8659},

DOI = {
10.1111/cgf.70406}

}
                
@article{
10.1111:cgf.70418,

journal = {Computer Graphics Forum},
title = {{
Magnetic Modeling and Simulation for Computer Graphics}},

author = {
Ni, Xingyu
 and 
Zhu, Yuechen
 and 
Wang, Ruicheng
 and 
Wang, Bin
},
year = {
2026},

publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},


ISSN = {1467-8659},

DOI = {
10.1111/cgf.70418}

}
                
@article{
10.1111:cgf.70393,

journal = {Computer Graphics Forum},
title = {{
EUROGRAPHICS 2026: CGF 45-2 STARs Frontmatter}},

author = {
Bönsch, Andrea
 and 
Umetani, Nobuyuki
},
year = {
2026},

publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},


ISSN = {1467-8659},

DOI = {
10.1111/cgf.70393}

}
@inproceedings{
10.1111:cgf.70393,

booktitle = {
Computer Graphics Forum},
Volume 45, Issue 2},
e70393},

editor = {
Bönsch, Andrea
 and 
Umetani, Nobuyuki
},
title = {{
EUROGRAPHICS 2026: CGF 45-2 STARs Frontmatter}},

author = {
Bönsch, Andrea
 and 
Umetani, Nobuyuki
},
year = {
2026},

publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
},

ISSN = {1467-8659},

DOI = {
10.1111/cgf.70393}

}
@inproceedings{
10.1111:cgf.70393,

booktitle = {
Computer Graphics Forum},
Volume 45, Issue 2},
e70393},

editor = {
Bönsch, Andrea
 and 
Umetani, Nobuyuki
},
title = {{
EUROGRAPHICS 2026: CGF 45-2 STARs Frontmatter}},

author = {
Bönsch, Andrea
 and 
Umetani, Nobuyuki
},
year = {
2026},

publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
},

ISSN = {1467-8659},

DOI = {
10.1111/cgf.70393}

}
                
@article{
10.1111:cgf.70394,

journal = {Computer Graphics Forum},
title = {{
Survey on differential estimators for 3d point clouds}},

author = {
Arnal-Anger, Léo
 and 
Lejemble, Thibault
 and 
Coeurjolly, David
 and 
Barthe, Loïc
 and 
Mellado, Nicolas
},
year = {
2026},

publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},


ISSN = {1467-8659},

DOI = {
10.1111/cgf.70394}

}

Browse

Recent Submissions

Now showing 1 - 8 of 8
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    Advances in Neural 3D Mesh Texturing: A Survey
    (The Eurographics Association and John Wiley & Sons Ltd., 2026) Perla, Sai Raj Kishore; Zhang, Hao ; Mahdavi-Amiri, Ali ; Bönsch, Andrea; Umetani, Nobuyuki
    Texturing 3D meshes plays a vital role in determining the visual realism of digital objects and scenes. Although recent generative 3D approaches based on Neural Radiance Fields and Gaussian Splatting can produce textured assets directly, polygonal meshes remain the core representation across modeling, animation, visual effects, and gaming pipelines. Neural 3D mesh texturing therefore continues to be an essential and active area of research. In this survey, we present a comprehensive review of recent advances in neural 3D mesh texturing, covering methods for texture synthesis, transfer, and completion. We first summarize key foundations in mesh geometry, texture mapping, differentiable rendering, and neural generative models, and then organize the literature into a unified taxonomy spanning early GAN-based methods to modern diffusion-based pipelines. We further analyze common architectures and supervision strategies, review datasets and evaluation protocols, and discuss emerging applications, practical and commercial systems, and open challenges. Together, these insights provide a structured perspective on the current landscape and help guide future developments in learning-based 3D mesh texturing. Project Page: sairajk.github.io/neural-mesh-texturing
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    How to Build Digital Humans? From Priors to Photorealistic Avatars
    (The Eurographics Association and John Wiley & Sons Ltd., 2026) Zielonka, Wojciech ; Kirschstein, Tobias ; Bolkart, Timo; Giebenhain, Simon ; Sklyarova, Vanessa ; Deng, Xiang ; Xiang, Donglai; Saito, Shunsuke; Liu, Yebin; Niessner, Matthias; Thies, Justus ; Bönsch, Andrea; Umetani, Nobuyuki
    This state-of-the-art report provides an overview of controllable 3D human avatar creation. We describe current 3D avatar systems, which typically consist of three stages: (i) learning priors of human appearance and motion, (ii) creating a personalized avatar, and (iii) animating the avatar. To limit the scope, we focus on the prior learning and avatar creation stages. We define current avatar representations and introduce a taxonomy that categorizes existing work along multiple axes, including body regions and employed priors. We review methods for full-body and head avatars, as well as layered representations that decompose the body into components such as hands, hair, and garments. Finally, we outline common underlying principles, reference key literature for newcomers, and discuss open challenges and future research directions.
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    State-of-the-art in deep learning approaches for automatic single-panorama indoor modeling and exploration
    (The Eurographics Association and John Wiley & Sons Ltd., 2026) Pintore, Giovanni; Agus, Marco; Schneider, Jens; Gobbetti, Enrico; Bönsch, Andrea; Umetani, Nobuyuki
    A single surround-view panoramic image provides complete coverage of the environment visible from a single viewpoint and inherently supports dynamic exploration, especially when viewed through a head-mounted display. For these reasons, single or linked 360-degree panoramas have become a widely adopted modality for indoor scene acquisition and virtual tour creation. Despite their popularity, panoramas present inherent limitations, as they only statically represent the captured scene, do not provide explicit 3D architectural structure and geometry, and exhibit minimal parallax due to their single-viewpoint nature, which limits their application capabilities or requires significant modeling efforts to generate missing data. In this survey, we provide an up-to-date integrative overview of recent techniques designed to overcome these challenges, bringing together complementary perspectives from machine learning, computer vision, and computer graphics. After introducing a characterization of the panoramic input and the target geometric, structural, and visual outputs, we discuss the role of reconstruction priors and motivate the choice of deep learning approaches for leveraging large-scale data to infer hidden information. Next, we outline the main sub-problems involved in lifting a 360-degree image into a structured, explorable model and review advances in single-view pixel-wise geometric and semantic analysis, single-view indoor layout estimation, localization and multi-room reconstruction from very sparse coverage, novel view synthesis for providing parallax, and immersive model exploration. We then discuss the emergence of both general-purpose and 360-degree-specific vision foundation models for single-panorama indoor modeling and exploration. Finally, we highlight practical applications and identify open research directions.
  • Thumbnail Image
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    Non-Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunities
    (The Eurographics Association and John Wiley & Sons Ltd., 2026) Zhuravlev, Aleksei; Bastian, Lennart; Cao, Dongliang; El Amrani, Nafie; Roetzer, Paul; Ehm, Viktoria; Marin, Riccardo; Nishizawa, Hiroki; Morishima, Shigeo; Theobalt, Christian; Navab, Nassir; Cremers, Daniel; Bernard, Florian; Lähner, Zorah; Golyanik, Vladislav; Bönsch, Andrea; Umetani, Nobuyuki
    Estimating correspondences between deformed shape instances is a long-standing problem in computer graphics; numerous applications, from texture transfer to statistical modelling, rely on recovering an accurate correspondence map. Many methods have thus been proposed to tackle this challenging problem from varying perspectives, depending on the downstream application. This state-of-the-art report is geared towards researchers, practitioners, and students seeking to understand recent trends and advances in the field. We categorise developments into three paradigms: spectral methods based on functional maps, combinatorial formulations that impose discrete constraints, and deformation-based methods that directly recover a global alignment. Each school of thought offers different advantages and disadvantages, which we discuss throughout the report. Meanwhile, we highlight the latest developments in each area and suggest new potential research directions. Finally, we provide an overview of emerging challenges and opportunities in this growing field, including the recent use of vision foundation models for zero-shot correspondence and the particularly challenging task of matching partial shapes.
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    Establishing Shape Correspondences: A Survey
    (The Eurographics Association and John Wiley & Sons Ltd., 2026) Heuschling, Alexandra; Meinhold, Hannah; Kobbelt, Leif; Bönsch, Andrea; Umetani, Nobuyuki
    Shape correspondence between surfaces in 3D is a central problem in geometry processing, concerned with establishing meaningful relations between surfaces. While all correspondence problems share this goal, specific formulations can differ significantly: downstream applications require certain properties that correspondences must satisfy, while the type of input data and computational constraints influence the choice of method. In this survey, we provide an overview of different correspondence problems, popular paradigms for generating and refining correspondences, and strategies for evaluating their quality. Further, we discuss topological aspects that are especially important for correspondences between surfaces with higher genus. By offering a structured overview and highlighting open challenges, we aim to support and guide future research in the field.
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    Magnetic Modeling and Simulation for Computer Graphics
    (The Eurographics Association and John Wiley & Sons Ltd., 2026) Ni, Xingyu; Zhu, Yuechen; Wang, Ruicheng; Wang, Bin; Bönsch, Andrea; Umetani, Nobuyuki
    Physics-based modeling and simulation have long been central to computer graphics, enabling the creation of realistic and expressive digital worlds. While extensive progress has been made in simulating physical phenomena of various materials, magnetic interactions, characterized by their long-range, nonlinear, and material-dependent nature, have only recently attracted sustained attention. Emerging research has explored magnetic effects across rigid bodies, deformable solids, ferrofluids, and magneto-viscoelastic materials, leading to a diverse set of models and solvers. However, these efforts have often evolved in isolation, leaving the broader landscape fragmented. This survey provides the first consolidated perspective on magnetic modeling and simulation for computer graphics. We begin by revisiting the fundamentals of magnetostatics and magneto-mechanical coupling, summarizing the governing equations that underlie existing methods. Building on this foundation, we review numerical algorithms in two complementary aspects: those addressing magnetic fields and magnetization, and those focused on force computation and integration with simulation frameworks. By clarifying common principles and comparing methodological choices, we discuss the successes and challenges of existing literature and outline promising directions for advancing the modeling and simulation of magnetic phenomena.
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    EUROGRAPHICS 2026: CGF 45-2 STARs Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2026) Bönsch, Andrea; Umetani, Nobuyuki; Bönsch, Andrea; Umetani, Nobuyuki
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    Survey on differential estimators for 3d point clouds
    (The Eurographics Association and John Wiley & Sons Ltd., 2026) Arnal-Anger, Léo; Lejemble, Thibault; Coeurjolly, David; Barthe, Loïc; Mellado, Nicolas; Bönsch, Andrea; Umetani, Nobuyuki
    Recent advancements in 3D scanning technologies, including LiDAR and photogrammetry, have enabled the precise digital replication of real-world objects. These methods are widely used in fields such as GIS, robotics, and cultural heritage. However, the point clouds generated by such scans are often noisy and unstructured, posing challenges for traditional geometry processing tasks. Accurately estimating differential properties like surface curvatures and normals is crucial for tasks such as shape matching and classification, but remains complex due to these inherent challenges. This paper reviews state-of-the-art methods for estimating differential properties from 3D point clouds, with a focus on approaches that offer strong mathematical foundations and theoretical guarantees. We also benchmark these methods using various datasets, evaluating their performance in terms of accuracy, robustness, and efficiency. Our contributions include the release of datasets, tools, and code to promote reproducibility and support future research in this area.