Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting

[2503.09640] Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Graphics arXiv:2503.09640 (cs) [Submitted on 12 Mar 2025 ( v1 ), last revised 9 Apr 2026 (this version, v2)] Title: Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting Authors: Weiquan Wang , Jun Xiao , Yi Yang , Yueting Zhuang , Long Chen View a PDF of the paper titled Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting, by Weiquan Wang and 4 other authors View PDF HTML (experimental) Abstract: Rendering realistic human-object interactions (HOIs) from sparse-view inputs is a challenging yet crucial task for various real-world applications. Existing methods often struggle to simultaneously achieve high rendering quality, physical plausibility, and computational efficiency. To address these limitations, we propose HOGS (Human-Object Rendering via 3D Gaussian Splatting), a novel framework for efficient HOI rendering with physically plausible geometric constraints from sparse views. HOGS represents both humans and objects as dynamic 3D Gaussians. Central to HOGS is a novel optimization process that operates directly on these Gaussians to enforce geometric consistency (i.e., preventing inter-penetration or floating contacts) to achieve physical plausibility. To support this core optimization under sparse-view ambiguity, our framework incorporates two pre-trained modules: an optimization-guided Human Pose Refiner for robust estimation under sparse-view occlusions, and a Human-Object Contact Predictor that efficiently identifies interaction regions to guide our novel contact and separation losses. Extensive experiments on both human-object and hand-object interaction datasets demonstrate that HOGS achieves state-of-the-art rendering quality and maintains high computational efficiency. Comments: 16 pages, 14 figures, accepted by IEEE Transactions on Image Processing (TIP) Subjects: Graphics (cs.GR) ; Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2503.09640 [cs.GR] (or arXiv:2503.09640v2 [cs.GR] for this version) https://doi.org/10.48550/arXiv.2503.09640 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Weiquan Wang [ view email ] [v1] Wed, 12 Mar 2025 04:19:21 UTC (4,372 KB) [v2] Thu, 9 Apr 2026 03:04:43 UTC (6,323 KB) Full-text links: Access Paper: View a PDF of the paper titled Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting, by Weiquan Wang and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.GR < prev | next > new | recent | 2025-03 Change to browse by: cs cs.CV References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Prune Wisely, Reconstruct Sharply: Compact 3D Gaussian Splatting via Adaptive Pruning and Difference-of-Gaussian Primitives#819306
• Prune Wisely, Reconstruct Sharply: Compact 3D Gaussian Splatting via Adaptive Pruning and Difference-of-Gaussian Primitives#809313
• 3D Gaussian Splatting with Fisheye Images: Field of View Analysis and Depth-Based Initialization#609811
• 2Xplat: Decoupling Geometry and Appearance Modeling for Feed-Forward 3D Gaussian Splatting#635777
• 2Xplat: Decoupling Geometry and Appearance Modeling for Feed-Forward 3D Gaussian Splatting#633428