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- W3166989905 abstract "This paper addresses the problem of 3D hand pose estimation from a monocular RGB image. While previous methods have shown great success, the structure of hands has not been fully exploited, which is critical in pose estimation. To this end, we propose a regularized graph representation learning under a conditional adversarial learning framework for 3D hand pose estimation, aiming to capture structural inter-dependencies of hand joints. In particular, we estimate an initial hand pose from a parametric hand model as a prior of hand structure, which regularizes the inference of the structural deformation in the prior pose for accurate graph representation learning via residual graph convolution. To optimize the hand structure further, we propose two bone-constrained loss functions, which characterize the morphable structure of hand poses explicitly. Also, we introduce an adversarial learning framework conditioned on the input image with a multi-source discriminator, which imposes the structural constraints onto the distribution of generated 3D hand poses for anthropomorphically valid hand poses. Extensive experiments demonstrate that our model sets the new state-of-the-art in 3D hand pose estimation from a monocular image on five standard benchmarks." @default.
- W3166989905 created "2021-06-22" @default.
- W3166989905 creator A5070765111 @default.
- W3166989905 creator A5080153028 @default.
- W3166989905 date "2019-12-04" @default.
- W3166989905 modified "2023-09-26" @default.
- W3166989905 title "3D Hand Pose Estimation via Regularized Graph Representation Learning" @default.
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- W3166989905 doi "https://doi.org/10.48550/arxiv.1912.01875" @default.
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