We present a novel framework based on Pictorial Structure (PS) models to estimate 2D multi-hand poses from depth images. Most existing single-hand pose estimation algorithms are either subject to strong assumptions or depend on a weak detector to detect the human hand. We utilize Mask R-CNN to avoid both aforementioned constraints. The proposed framework allows detection of multi-hand instances and localization of hand joints simultaneously. Our experiments show that our method is superior to existing methods.
CITATION STYLE
Duan, L., Shen, M., Cui, S., Guo, Z., & Deussen, O. (2019). Estimating 2D multi-hand poses from single depth images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11134 LNCS, pp. 257–272). Springer Verlag. https://doi.org/10.1007/978-3-030-11024-6_17
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