RiCo is a neural simulation framework that models rigid‑body interactions by focusing on sparse neighborhoods of contact surface points. It integrates each point’s state with the geometry, motion, and physical properties of nearby surfaces, enabling local reasoning that preserves fine‑grained interaction details without modeling every pair of scene points. Experiments on the MOVi benchmark show that RiCo reduces 100‑frame position and orientation errors by 31‑35% and 38%, respectively, while achieving high contact fidelity and zero‑shot generalization to scenes with 270 objects.
By Ruixiang Ouyang, Guanren Qiao, Fansen Meng, Yueci Deng, Ruixing Jin, Kui Jia, Guiliang Liu
arXiv:2607. 04234v1 Announce Type: cross Abstract: Deformable object manipulation poses challenges beyond task completion: successful execution must also maintain safe physical interaction, holding the object stably without slip or drop while avoiding excessive deformation.
By Bowen Jing, Mingxin Wang, Ruiyang Hao, Chenchen Ge, Hanwen Shen, Junjie He, Yang Cui, Yiming Hou, Weitao Zhou, Jiawei Wang, Minglei Li, Dandan Zhang, Ding Zhao, Houde Liu, Xiaofan Li, Si Liu, Ping Luo, Haibao Yu
arXiv:2607. 04234v2 Announce Type: replace-cross Abstract: Deformable object manipulation poses challenges beyond task completion: successful execution must also maintain safe physical interaction, holding the object stably without slip or drop while avoiding excessive deformation.
By Bowen Jing, Mingxin Wang, Ruiyang Hao, Chenchen Ge, Hanwen Shen, Junjie He, Yang Cui, Yiming Hou, Weitao Zhou, Jiawei Wang, Minglei Li, Dandan Zhang, Ding Zhao, Houde Liu, Xiaofan Li, Si Liu, Ping Luo, Haibao Yu
AffordSim is a scalable data generator and benchmark that integrates open‑vocabulary 3D affordance prediction into simulation‑based trajectory generation for robotic manipulation. Given a natural‑language task description, it synthesizes task‑relevant scenes, queries affordances, grounds them on object surfaces, samples region‑conditioned grasps, and selects executable candidates with motion planning, while randomizing pose, texture, lighting, noise, and backgrounds for sim‑to‑real transfer. The benchmark includes 50 tasks, five robot embodiments, and over 500 objects, achieving 93% of the success rate of manual contact annotations on affordance‑critical tasks and 89% on hard composite tasks, with policies trained on AffordSim transferring zero‑shot to a real Franka FR3 with 24% average success.
By Mingyang Li, Haofan Xu, Haowen Sun, Xinzhe Chen, Sihua Ren, Liqi Huang, Xinyang Sui, Chenyang Miao, Jiawei Ye, Qiongjie Cui, Xingyu Chen, Zeyang Liu, Xuguang Lan
arXiv:2608.13014v2 Announce Type: replace
Abstract: Understanding hand-object interaction from egocentric vision is essential for modeling how people physically engage with the surrounding world. Yet...
By Andela Ilic, Rachel Schuchert, Yijing Jiang, Christian Holz
Predicting object dynamics (i. e.
ScIntBench is a new benchmark for evaluating text‑conditioned 3D indoor scene generation, focusing on interaction readiness rather than just static physical plausibility. It runs prescribed manipulation actions—such as pushing, opening, and probing—in a physics simulation and records metrics like task success, capability, progress, post‑interaction integrity, and dynamic plausibility, supplemented by multi‑view semantic and static physical checks. Across 12 state‑of‑the‑art systems and 10 room types, the study finds that high static plausibility does not guarantee interaction readiness, with the best system succeeding in only 36.92% of tasks and many systems unable to support any interaction at all.
By Wendi Ying, Daochang Liu, Leonid Sigal, Ajmal Saeed Mian
arXiv:2606.21596v2 Announce Type: replace
Abstract: Recent image-to-3D scene methods recover high-fidelity 3D objects with plausible arrangements, but often leave floatings and interpenetrations that...
By Haodong Li, Lulu Shao, Haolin Lu, Yu Fu, Yen-Ru Chen, Seemandhar Jain, Manmohan Chandraker
arXiv:2610.28280v1 Announce Type: cross
Abstract: Reasoning about fine-grained placement constraints, such as support and containment, is essential for realistic 3D scene creation. It remains a signi...
By Chucheng Xiang, Runze Wang, Zhi Deng, Ruchao Bao, Yuanwei Zhang, Yuxuan Xie, Hanliu Wang, Liangzhen Fei, Wenzheng Wu, Cheng Wan, Peifeng Li, Zhongyuan Liu, Ligang Liu
arXiv:2610.20560v1 Announce Type: cross
Abstract: 3D object composition is pivotal to digital content creation and robot simulation, while also serving as a critical testbed for physical intelligence...
By Weichen Lyu, Chong Xia, Mingyu Dong, Long Xu, Shengjun Zhang, Yueqi Duan
arXiv:2609.36024v1 Announce Type: new
Abstract: Reconstructing simulation-ready 3D scenes from real-world observations enables robotics, gaming, and immersive applications, yet existing methods large...
By Shuzhao Xie, Lelin Wang, Guying Lin, Zhi Wang, Minchen Li
arXiv:2610.07460v1 Announce Type: cross
Abstract: Inserting objects into existing 3D scenes requires more than selecting a plausible location:
the inserted object must also fit local geometry while...
By Tzu-Hsin Hsieh, Ricardo Marroquim