[CVPR24] Scaling Up Dynamic Human-Scene Interaction Modeling
Abstract
Confronting the challenges of data scarcity and advanced motion synthesis in human-scene interaction modeling, we introduce the TRUMANS dataset alongside a novel HSI motion synthesis method. TRUMANS stands as the most comprehensive motion-captured HSI dataset currently available, encompassing over 15 hours of human interactions across 100 indoor scenes. It intricately captures whole-body human motions and part-level object dynamics, focusing on the realism of contact. This dataset is further scaled up by transforming physical environments into exact virtual models and applying extensive augmentations to appearance and motion for both humans and objects while maintaining interaction fidelity. Utilizing TRUMANS, we devise a diffusion-based autoregressive model that efficiently generates HSI sequences of any length, taking into account both scene context and intended actions. In experiments, our approach shows remarkable zero-shot generalizability on a range of 3D scene datasets (e.g., PROX, Replica, ScanNet, ScanNet++), producing motions that closely mimic original motion-captured sequences, as confirmed by quantitative experiments and human studies.
Nan Jiang
Ph.D. '22
Hongjie Li
Zhi Class '21
My research interests include human-object interaction and scene understanding in 3D computer vision.
Xiaoxuan Ma
Ph.D. '21, co-advised with Prof. Yizhou Wang
My research interests include computer vision and machine learning.
Yixin Chen
Research Scientist
Tengyu Liu
Research Scientist
