We propose a novel learned deep prior of body motion for 3D hand shape
synthesis and estimation in the domain of conversational gestures. Our model
builds upon the insight that body motion and hand gestures are strongly
correlated in non-verbal communication settings. We formulate the learning of
this prior as a prediction task of 3D hand shape over time given body motion
input alone. Trained with 3D pose estimations obtained from a large-scale
dataset of internet videos, our hand prediction model produces convincing 3D
hand gestures given only the 3D motion of the speaker's arms as input. We
demonstrate the efficacy of our method on hand gesture synthesis from body
motion input, and as a strong body prior for single-view image-based 3D hand
pose estimation. We demonstrate that our method outperforms previous
state-of-the-art approaches and can generalize beyond the monologue-based
training data to multi-person conversations. Video results are available at
this http URL

本文提出了一种基于学习深度先验的新型 3D 手部形态综合和估计方法，其中利用了身体运动和手势之间的相关性，采用网络进行 3D 手部姿态估计训练并得到了优良的效果。