تقرير
Multi-view Hand Reconstruction with a Point-Embedded Transformer
| العنوان: | Multi-view Hand Reconstruction with a Point-Embedded Transformer |
|---|---|
| المؤلفون: | Yang, Lixin, Zhong, Licheng, Zhu, Pengxiang, Zhan, Xinyu, Kong, Junxiao, Xu, Jian, Lu, Cewu |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: Generalizable multi-view Hand Mesh Reconstruction (HMR) model. Extension of the original work at CVPR2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.10581 |
| رقم الأكسشن: | edsarx.2408.10581 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |