HandVoxNet++: 3D Hand Shape and Pose Estimation Using Voxel-Based Neural Networks

@article{Malik2021HandVoxNet3H,
  title={HandVoxNet++: 3D Hand Shape and Pose Estimation Using Voxel-Based Neural Networks},
  author={Jameel Malik and Soshi Shimada and Ahmed Elhayek and Sk Aziz Ali and Christian Theobalt and Vladislav Golyanik and Didier Stricker},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
  volume={44},
  pages={8962-8974}
}
3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. Existing methods addressing it directly regress hand meshes via 2D convolutional neural networks, which leads to artifacts due to perspective distortions in the images. To address the limitations of the existing methods, we develop HandVoxNet++, i.e., a voxel-based deep network with 3D and graph convolutions trained in a fully supervised manner. The input to our… 

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