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.

通过引入 TRUMANS 数据集和一种新颖的 HSI 运动合成方法，我们克服了数据稀缺和高级动作合成在人类 - 场景交互建模中的挑战。我们的方法采用扩展的扩展的物理环境模型和外观运动增强技术，使 TRUMANS 数据集成为目前最全面的 HSI 数据集，能够生成任意长度的 HSI 序列，并在各种 3D 场景数据集上表现出优异的泛化能力。