Realistic Full-Body Tracking from Sparse Observations via Joint-Level Modeling

• URL: http://arxiv.org/abs/2308.08855v1
• Date: Thu, 17 Aug 2023 08:27:55 GMT
• Title: Realistic Full-Body Tracking from Sparse Observations via Joint-Level Modeling
• Authors: Xiaozheng Zheng, Zhuo Su, Chao Wen, Zhou Xue, Xiaojie Jin
• Abstract summary: We propose a two-stage framework that can obtain accurate and smooth full-body motions with three tracking signals of head and hands only. Our framework explicitly models the joint-level features in the first stage and utilizes them astemporal tokens for alternating spatial and temporal transformer blocks to capture joint-level correlations in the second stage. With extensive experiments on the AMASS motion dataset and real-captured data, we show our proposed method can achieve more accurate and smooth motion compared to existing approaches.
• Score: 13.284947022380404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To bridge the physical and virtual worlds for rapidly developed VR/AR applications, the ability to realistically drive 3D full-body avatars is of great significance. Although real-time body tracking with only the head-mounted displays (HMDs) and hand controllers is heavily under-constrained, a carefully designed end-to-end neural network is of great potential to solve the problem by learning from large-scale motion data. To this end, we propose a two-stage framework that can obtain accurate and smooth full-body motions with the three tracking signals of head and hands only. Our framework explicitly models the joint-level features in the first stage and utilizes them as spatiotemporal tokens for alternating spatial and temporal transformer blocks to capture joint-level correlations in the second stage. Furthermore, we design a set of loss terms to constrain the task of a high degree of freedom, such that we can exploit the potential of our joint-level modeling. With extensive experiments on the AMASS motion dataset and real-captured data, we validate the effectiveness of our designs and show our proposed method can achieve more accurate and smooth motion compared to existing approaches.

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