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Abstract

We present Mask2Hand, a self-trainable method for predicting 3D hand pose and shape from a single 2D binary silhouette. Without additional manual annotations, our method uses differentiable rendering to project 3D estimations onto the 2D silhouette. A tailored loss function, applied between the rendered and input silhouettes, provides a self-guidance mechanism during end-to-end optimization, which constrains global mesh registration and hand pose estimation. Our experiments show that Mask2Hand, using only a binary mask input, achieves accuracy comparable to state-ofthe- art methods requiring RGB or depth inputs on both unaligned and aligned datasets.