Uni-HOI is a unified framework that learns the joint distribution among text, human motion, and object motion for 4D human‑object interaction (HOI). It uses large language models and two motion‑specific VQ‑VAEs to convert heterogeneous motion data into token sequences, enabling seamless integration of all three modalities. A two‑stage training strategy first captures correlations on a large‑scale HOI dataset and then fine‑tunes for specific tasks, achieving strong performance on text‑driven HOI generation, object‑motion‑driven human motion generation, and human‑motion‑driven object motion prediction.
By Mengfei Zhang, Jinlu Zhang, Zhigang Tu
arXiv:2608. 16222v1 Announce Type: cross Abstract: Humanoid intelligence requires learning over an extremely diverse space of whole-body motions and physically grounded interactions.
By Jiahao Ji, Ji Ma, Runhan Zhang, Runyi Yu, Wenjia Wang, Weiheng Chi, Qianqian Peng, Weichao Yan, Yongfei Gu, Ye Tian, Ting Wu, Longwei Li, Chun Yuan, Ruoli Dai, Lei Han
arXiv:2610.36384v1 Announce Type: cross
Abstract: Perceiving human body motion is critical for multimodal systems that understand and assist people. Yet current evaluations of Multimodal Large Langua...
By Matea Tashkovska, Na\"el Ouerghemi, Haozhe Qi, Alexander Mathis
arXiv:2609.36937v1 Announce Type: cross
Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation,...
By Sangeyl Lee, Seunghyun Shin, Seungho Park, Wooseok Jeon, Hae-Gon Jeon
The paper introduces GALA, a Geometry-Aware Latent Action modeling framework that enhances image-based latent actions with 3D end‑effector motion. It proposes the Unified End‑effector Motion Representation (UEMR) to preserve fine‑grained motion while improving cross‑embodiment generalizability. Experiments show GALA effectively models generalizable fine‑grained motions across embodiments, achieving high success rates in RoboCasa-GR1 and real‑world tasks.
By Yichen Liu, Puzhen Yuan, Xiang Zhu, Yanjiang Guo, Jianyu Chen
arXiv:2610.28405v1 Announce Type: new
Abstract: Recent advances in multimodal large language models (MLLMs) have substantially improved spoken interaction and video understanding. Existing online and...
By Yuhang Chen, Guangyue Liu, Shilong Liu
arXiv:2609.00369v1 Announce Type: new
Abstract: Generating co-speech gestures that are temporally coherent, semantically aligned with speech, and grounded with surrounding objects remains challenging...
By Vida Adeli, Soroush Mehraban, Jacob Rommann, Harrison Sanborn, Cole Clifford, Babak Taati
arXiv:2609.38615v1 Announce Type: cross
Abstract: Egocentric videos of human manipulation provide valuable visual experience for embodied intelligence, yet collecting such data at scale is costly. Ex...
By Hongjia Zhai, Xiyu Zhang, Haoran Zhang, Zhichao Ye, Haomin Liu, Guofeng Zhang, Ian Reid, Xingxing Zuo
arXiv:2610.28523v1 Announce Type: cross
Abstract: Humanoid-human interaction requires robots to operate under severe partial observability: during close physical contact, the human partner is occlude...
By Yuto Shibata, Kotaro Amaya, Kashu Yamazaki, Lalit Jayanti, Yoshimitsu Aoki, Mariko Isogawa, Katerina Fragkiadaki
arXiv:2602.13444v2 Announce Type: replace-cross
Abstract: Recent vision-language-action (VLA) models can generate plausible end-effector motions, yet they often fail in long-horizon, contact-rich tas...
By Huajian Zeng, Lingyun Chen, Jiaqi Yang, Yuantai Zhang, Fan Shi, Peidong Liu, Xingxing Zuo
arXiv:2607. 17342v1 Announce Type: cross Abstract: Understanding physical human-robot and human-human interactions is a challenging yet emerging topic in 3D vision.
By Yuhang Wen, Mengyuan Liu, Zixuan Tang, Junsong Yuan, Sirui Li, Beichen Ding
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