{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/interdiff-generating-3d-human-object","title":"InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion","arxiv_id":"2308.16905","date":"2023-08-31","proceeding":"ICCV 2023 1","authors":["Sirui Xu","Zhengyuan Li","Yu-Xiong Wang","Liang-Yan Gui"],"abstract":"This paper addresses a novel task of anticipating 3D human-object interactions (HOIs). Most existing research on HOI synthesis lacks comprehensive whole-body interactions with dynamic objects, e.g., often limited to manipulating small or static objects. Our task is significantly more challenging, as it requires modeling dynamic objects with various shapes, capturing whole-body motion, and ensuring physically valid interactions. To this end, we propose InterDiff, a framework comprising two key steps: (i) interaction diffusion, where we leverage a diffusion model to encode the distribution of future human-object interactions; (ii) interaction correction, where we introduce a physics-informed predictor to correct denoised HOIs in a diffusion step. Our key insight is to inject prior knowledge that the interactions under reference with respect to contact points follow a simple pattern and are easily predictable. Experiments on multiple human-object interaction datasets demonstrate the effectiveness of our method for this task, capable of producing realistic, vivid, and remarkably long-term 3D HOI predictions.","url_abs":"https://arxiv.org/abs/2308.16905v1","url_pdf":"https://arxiv.org/pdf/2308.16905v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"interdiff-generating-3d-human-object","repo_url":"https://github.com/Sirui-Xu/InterDiff","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-human-dynamics","task_name":"3D Human Dynamics"},{"task_slug":"human-dynamics","task_name":"Human Dynamics"},{"task_slug":"human-pose-forecasting","task_name":"Human Pose Forecasting"},{"task_slug":"human-motion-prediction","task_name":"Human motion prediction"},{"task_slug":"human-object-interaction-detection","task_name":"Human-Object Interaction Detection"},{"task_slug":"motion-synthesis","task_name":"Motion Synthesis"},{"task_slug":"object","task_name":"Object"},{"task_slug":"short-term-object-interaction-anticipation","task_name":"Short-term Object Interaction Anticipation"},{"task_slug":null,"task_name":"valid"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2308.16905","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.16905"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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