Papers › 3D Hand Shape and Pose from Images in the Wild

3D Hand Shape and Pose from Images in the Wild

9 Feb 2019CVPR 2019 6arXiv:1902.03451archive 2025-07-28

Adnane Boukhayma, Rodrigo de Bem, Philip H. S. Torr

We present in this work the first end-to-end deep learning based method that predicts both 3D hand shape and pose from RGB images in the wild. Our network consists of the concatenation of a deep convolutional encoder, and a fixed model-based decoder. Given an input image, and optionally 2D joint detections obtained from an independent CNN, the encoder predicts a set of hand and view parameters. The decoder has two components: A pre-computed articulated mesh deformation hand model that generates a 3D mesh from the hand parameters, and a re-projection module controlled by the view parameters that projects the generated hand into the image domain. We show that using the shape and pose prior knowledge encoded in the hand model within a deep learning framework yields state-of-the-art performance in 3D pose prediction from images on standard benchmarks, and produces geometrically valid and plausible 3D reconstructions. Additionally, we show that training with weak supervision in the form of 2D joint annotations on datasets of images in the wild, in conjunction with full supervision in the form of 3D joint annotations on limited available datasets allows for good generalization to 3D shape and pose predictions on images in the wild.

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boukhayma/3dhand mentioned on GitHubpytorch report
yihui-he/epipolar-transformers mentioned on GitHubpytorchMIT report

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Tasks

3D Hand Pose EstimationDecoderPose Prediction

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Hand Pose Estimation FreiHAND Boukhayma et al. PA-F@15mm 0.898 #32 of 33 Archive leaderboard report
3D Hand Pose Estimation FreiHAND Boukhayma et al. PA-F@5mm 0.435 #32 of 33 Archive leaderboard report
3D Hand Pose Estimation FreiHAND Boukhayma et al. PA-MPVPE 13.0 #32 of 33 Archive leaderboard report

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