Single Image 3D Hand Reconstruction with Mesh Convolutions

By Dominik Kulon, Haoyang Wang, Riza Alp Güler, Michael M. Bronstein, Stefanos P. Zafeiriou. 2019

68 citations recorded by the source corpus.

Monocular 3D reconstruction of deformable objects, such as human body parts, has been typically approached by predicting parameters of heavyweight linear models. In this paper, we demonstrate an alternative solution that is based on the idea of encoding images into a latent non-linear representation of meshes. The prior on 3D hand shapes is learned by training an autoencoder with intrinsic graph convolutions performed in the spectral domain. The pre-trained decoder acts as a non-linear statistical deformable model. The latent parameters that reconstruct the shape and articulated pose of hands in the image are predicted using an image encoder. We show that our system reconstructs plausible meshes and operates in real-time. We evaluate the quality of the mesh reconstructions produced by the decoder on a new dataset and show latent space interpolation results. Our code, data, and models will be made publicly available.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, 3D Vision & Pose

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Citations in the corpus

Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications (2022, background): “One highly related direction is to model and estimate 3D hand joints [5,44,45,53,60,63,66,72,78,79,80,85,3] and mesh [26,27,51,55,75,83,46], object pose [14,28,29,35,43,57,64,71,76,81], or both [6,17,18,19,69].”
Reconstructing Hand-Object Interactions in the Wild (2020, methodology): “Recently, [20, 19] proposes to use mesh convolution to directly predict 3D hand mesh reconstruction.”
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild (2020, background): “[27] reconstruct a hand from an image by regressing a latent vector of a pretrained mesh generator and estimating a camera projection.”