MoManifold: Learning to Measure 3D Human Motion via Decoupled Joint Acceleration Manifolds

• URL: http://arxiv.org/abs/2409.00736v1
• Date: Sun, 1 Sep 2024 15:00:16 GMT
• Title: MoManifold: Learning to Measure 3D Human Motion via Decoupled Joint Acceleration Manifolds
• Authors: Ziqiang Dang, Tianxing Fan, Boming Zhao, Xujie Shen, Lei Wang, Guofeng Zhang, Zhaopeng Cui,
• Abstract summary: We present MoManifold, a novel human motion prior, which models plausible human motion in continuous high-dimensional motion space. Specifically, we propose novel decoupled joint acceleration to model human dynamics from existing limited motion data. Extensive experiments demonstrate that MoManifold outperforms existing SOTAs as a prior in several downstream tasks.
• Score: 20.83684434910106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incorporating temporal information effectively is important for accurate 3D human motion estimation and generation which have wide applications from human-computer interaction to AR/VR. In this paper, we present MoManifold, a novel human motion prior, which models plausible human motion in continuous high-dimensional motion space. Different from existing mathematical or VAE-based methods, our representation is designed based on the neural distance field, which makes human dynamics explicitly quantified to a score and thus can measure human motion plausibility. Specifically, we propose novel decoupled joint acceleration manifolds to model human dynamics from existing limited motion data. Moreover, we introduce a novel optimization method using the manifold distance as guidance, which facilitates a variety of motion-related tasks. Extensive experiments demonstrate that MoManifold outperforms existing SOTAs as a prior in several downstream tasks such as denoising real-world human mocap data, recovering human motion from partial 3D observations, mitigating jitters for SMPL-based pose estimators, and refining the results of motion in-betweening.

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