MoManifold: Learning to Measure 3D Human Motion via Decoupled Joint Acceleration Manifolds
<|reference_start|>MoManifold: Learning to Measure 3D Human Motion via Decoupled Joint Acceleration Manifolds: 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.<|reference_end|>
@article{dang2024momanifold:,
    title={MoManifold: Learning to Measure 3D Human Motion via Decoupled Joint
  Acceleration Manifolds},
    author={Ziqiang Dang, Tianxing Fan, Boming Zhao, Xujie Shen, Lei Wang, Guofeng
  Zhang, Zhaopeng Cui},
    journal={arXiv preprint arXiv:2409.00736},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00736},
    primaryClass={cs.CV}
}