Multi-view Hand Reconstruction with a Point-Embedded Transformer
<|reference_start|>Multi-view Hand Reconstruction with a Point-Embedded Transformer: This work introduces a novel and generalizable multi-view Hand Mesh Reconstruction (HMR) model, named POEM, designed for practical use in real-world hand motion capture scenarios. The advances of the POEM model consist of two main aspects. First, concerning the modeling of the problem, we propose embedding a static basis point within the multi-view stereo space. A point represents a natural form of 3D information and serves as an ideal medium for fusing features across different views, given its varied projections across these views. Consequently, our method harnesses a simple yet effective idea: a complex 3D hand mesh can be represented by a set of 3D basis points that 1) are embedded in the multi-view stereo, 2) carry features from the multi-view images, and 3) encompass the hand in it. The second advance lies in the training strategy. We utilize a combination of five large-scale multi-view datasets and employ randomization in the number, order, and poses of the cameras. By processing such a vast amount of data and a diverse array of camera configurations, our model demonstrates notable generalizability in the real-world applications. As a result, POEM presents a highly practical, plug-and-play solution that enables user-friendly, cost-effective multi-view motion capture for both left and right hands. The model and source codes are available at https://github.com/JubSteven/POEM-v2.<|reference_end|>
@article{yang2024multi-view,
    title={Multi-view Hand Reconstruction with a Point-Embedded Transformer},
    author={Lixin Yang, Licheng Zhong, Pengxiang Zhu, Xinyu Zhan, Junxiao Kong,
  Jian Xu, Cewu Lu},
    journal={arXiv preprint arXiv:2408.10581},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10581},
    primaryClass={cs.CV}
}