← Back to all news
arXiv AI By Mingyuan Lei, Yoonchang Sung, Tat-Jen Cham

MAMHOI: Factorizing Scene-Aware Human-Object Interaction through Affordances

Read the original on arXiv AI →

MAMHOI introduces an affordance-mediated factorization for generating scene-aware human‑object interactions. It separates scene understanding and motion synthesis: a scene‑conditioned model predicts feasible interaction locations and modes, while an affordance‑conditioned model generates the corresponding human‑object motion. This approach enables learning from separate human‑scene and human‑object datasets, reducing object‑scene penetration and improving interaction realism in complex indoor environments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Computer Vision
Sep 22

AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation

arXiv:2609.22332v1 Announce Type: cross Abstract: Generalizable robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act...

By Jiadi You, Qize Yu, Yue Chen, Minghong Cai, Zhide Zhong, Yuran Wang, Bowen Ping, Jiaqi Liang, Zhenhao Shen, Haodong Yan, Yinchuan Li, Ruihai Wu, Xiaojuan Qi, Yingcong Chen
llmsdiffusionroboticsmultimodal
More like this →
arXiv Computer Vision
Oct 22

UniRAP: Towards Unified Part-level Physical Affordance Reasoning and Actionable Perception

UniRAP is a unified model that infers part-level physical affordances and translates language instructions into actionable geometric representations. It treats the task as a conditional multimodal generation problem, integrating visual inputs, textual instructions, and optional prompts into a shared spatiotemporal representation via a unified interface token mechanism. The model employs a Unified Affordance Decoder to jointly perform object detection, part-level affordance segmentation, and 4‑DoF interaction pose estimation, and uses a curriculum-based transfer training strategy to improve data efficiency with limited textual annotations.

By Linfei Li, Ruining Hu, Lin Zhang, Zhong Wang, Fengyi Zhang, Ying Shen, Binqiang Wang, Xin Zhang
computer-visionroboticsmultimodalbenchmarks
More like this →
arXiv AI
Oct 21

World2HOSI: A Multi-Expert Harness for Long-Horizon Human-Object-Scene Interaction Generation

World2HOSI is a multi‑expert harness that generates long‑horizon human‑object‑scene interaction (HOSI) data by orchestrating existing human‑object interaction (HOI) and human‑scene interaction (HSI) models. It builds a persistent scene state from decoupled background and object assets, plans action stages, invokes experts via their native interfaces, and updates the scene after each action, ensuring physical plausibility through bounded residual optimization and contact‑driven physics simulation. Experiments and a perceptual user study demonstrate that World2HOSI achieves superior task completion and interaction quality while producing more diverse data than prior approaches.

By Jiushu Li, Haoran Wang, Yumeng Liu, Zhiyang Dou, Yuan Liu, Yuexin Ma, Cheng Lin
agentsrobotics
More like this →