43 citations · 52 across the 14 of their papers we have counts for
15 papers
InterMimicGen: Scaling Humanoid Loco-Manipulation through Self-Evolving Motion Imitation
Yucheng Zhang, Sirui Xu, Jinhong Li +8
Captured human-object interactions provide rich supervision for humanoid loco-manipulation, but they are sparse, heterogeneous, and not directly executable by robots. We introduce…
HandX: Scaling Bimanual Motion and Interaction Generation
Zimu Zhang, Yucheng Zhang, Xiyan Xu +8
Synthesizing human motion has advanced rapidly, yet realistic hand motion and bimanual interaction remain underexplored. Whole-body models often miss the fine-grained cues that dri…
SlotVTG: Object-Centric Adapter for Generalizable Video Temporal Grounding
Jiwook Han, Geo Ahn, Youngrae Kim +1
Multimodal Large Language Models (MLLMs) have shown strong performance on Video Temporal Grounding (VTG). However, their coarse recognition capabilities are insufficient for fine-g…
ULTRA: Unified Multimodal Control for Autonomous Humanoid Whole-Body Loco-Manipulation
Xialin He, Sirui Xu, Xinyao Li +4
Achieving autonomous and versatile whole-body loco-manipulation remains a central barrier to making humanoids practically useful. Yet existing approaches are fundamentally constrai…
InterPrior: Scaling Generative Control for Physics-Based Human-Object Interactions
Sirui Xu, Samuel Schulter, Morteza Ziyadi +4
Humans rarely plan whole-body interactions with objects at the level of explicit whole-body movements. High-level intentions, such as affordance, define the goal, while coordinated…
BEAT: Visual Backdoor Attacks on VLM-based Embodied Agents via Contrastive Trigger Learning
Qiusi Zhan, Hyeonjeong Ha, Rui Yang +7
Recent advances in Vision-Language Models (VLMs) have propelled embodied agents by enabling direct perception, reasoning, and planning task-oriented actions from visual inputs. How…
MoReact: Generating Reactive Motion from Textual Descriptions
Xiyan Xu, Sirui Xu, Yu-Xiong Wang +1
Modeling and generating human reactions poses a significant challenge with broad applications for computer vision and human-computer interaction. Existing methods either treat mult…
Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration
Sirui Xu, Yu-Wei Chao, Liuyu Bian +4
Hand-object motion-capture (MoCap) repositories offer large-scale, contact-rich demonstrations and hold promise for scaling dexterous robotic manipulation. Yet demonstration inaccu…
InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generation
Sirui Xu, Dongting Li, Yucheng Zhang +9
While large-scale human motion capture datasets have advanced human motion generation, modeling and generating dynamic 3D human-object interactions (HOIs) remain challenging due to…
InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions
Sirui Xu, Hung Yu Ling, Yu-Xiong Wang +1
Achieving realistic simulations of humans interacting with a wide range of objects has long been a fundamental goal. Extending physics-based motion imitation to complex human-objec…
DICE: End-to-end Deformation Capture of Hand-Face Interactions from a Single Image
Qingxuan Wu, Zhiyang Dou, Sirui Xu +11
Reconstructing 3D hand-face interactions with deformations from a single image is a challenging yet crucial task with broad applications in AR, VR, and gaming. The challenges stem…
InterDreamer: Zero-Shot Text to 3D Dynamic Human-Object Interaction
Sirui Xu, Ziyin Wang, Yu-Xiong Wang +1
Text-conditioned human motion generation has experienced significant advancements with diffusion models trained on extensive motion capture data and corresponding textual annotatio…
InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion
Sirui Xu, Zhengyuan Li, Yu-Xiong Wang +1
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 dyna…
Stochastic Multi-Person 3D Motion Forecasting
Sirui Xu, Yu-Xiong Wang, Liang-Yan Gui
This paper aims to deal with the ignored real-world complexities in prior work on human motion forecasting, emphasizing the social properties of multi-person motion, the diversity…
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal Anchors
Sirui Xu, Yu-Xiong Wang, Liang-Yan Gui
Predicting diverse human motions given a sequence of historical poses has received increasing attention. Despite rapid progress, existing work captures the multi-modal nature of hu…