Scaling Up Dynamic Human-Scene Interaction Modeling
Nan Jiang Zhiyuan Zhang Hongjie Li Xiaoxuan Ma Zan Wang Yixin Chen Tengyu Liu Yixin Zhu Siyuan Huang
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.