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About this paper
Gyeongsik Moon, Shunsuke Saito, Weipeng Xu, Rohan Joshi, Julia Buffalini, Harley Bellan, Nicholas Rosen, Jesse Richardson and 7 more
Meta Reality Labs Research introduces Re:InterHand, a large-scale dataset of 1.5 million relighted 3D interacting hands designed to bridge the gap between accurate 3D ground truth and realistic image appearances. The dataset achieves this by combining high-fidelity 3D captures with neural relighting techniques, resulting in improved performance for hand pose estimation models on real-world data.
Key insights
- Neural relighting provides a method to combine accurate 3D ground truth from controlled lab environments with diverse and realistic image appearances for training data.
- Utilizing a 3D joint detection network (V2V-PoseNet) on voxelized 3D scans enhances the temporal consistency of 3D joint coordinates, leading to more accurate hand model fits.
- A dataset featuring diverse interacting hand poses, accurate 3D annotations, and realistic lighting generated from numerous environment maps is essential for developing robust hand pose estimation algorithms.
Summary by alphaXiv.
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