SyncDiff: 同期運動拡散によるマルチボディ人間-物体インタラクション合成
SyncDiff: Synchronized Motion Diffusion for Multi-Body Human-Object Interaction Synthesis
複数の人間・手・物体が関与するマルチボディのインタラクション運動を、同期運動拡散モデルで生成する手法を提案。周波数領域分解とアラインメントスコアで運動の同期性と忠実度を高めた。
著者: Wenkun He, Yun Liu, Ruitao Liu, Li Yi
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
Synthesizing realistic human-object interaction motions is a critical problem in VR/AR and human animation. Unlike the commonly studied scenarios involving a single human or hand interacting with one object, we address a more generic multi-body setting with arbitrary numbers of humans, hands, and objects. This complexity introduces significant challenges in synchronizing motions due to the high correlations and mutual influences among bodies. To address these challenges, we introduce SyncDiff, a novel method for multi-body interaction synthesis using a synchronized motion diffusion strategy. SyncDiff employs a single diffusion model to capture the joint distribution of multi-body motions. To enhance motion fidelity, we propose a frequency-domain motion decomposition scheme. Additionally, we introduce a new set of alignment scores to emphasize the synchronization of different body motions. SyncDiff jointly optimizes both data sample likelihood and alignment likelihood through an explicit synchronization strategy. Extensive experiments across four datasets with various multi-body configurations demonstrate the superiority of SyncDiff over existing state-of-the-art motion synthesis methods.
