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We propose a novel framework to reconstruct accurate appearance and geometrywith neural radiance fields (NeRF) for interacting hands, enabling therendering of photo-realistic images and videos for gesture animation fromarbitrary views. Given multi-view images of a single hand or interacting hands,an off-the-shelf skeleton estimator is first employed to parameterize the handposes. Then we design a pose-driven deformation field to establishcorrespondence from those different poses to a shared canonical space, where apose-disentangled NeRF for one hand is optimized. Such unified modelingefficiently complements the geometry and texture cues in rarely-observed areasfor both hands. Meanwhile, we further leverage the pose priors to generatepseudo depth maps as guidance for occlusion-aware density learning. Moreover, aneural feature distillation method is proposed to achieve cross-domainalignment for color optimization. We conduct extensive experiments to verifythe merits of our proposed HandNeRF and report a series of state-of-the-artresults both qualitatively and quantitatively on the large-scale InterHand2.6Mdataset.

HandNeRF: Neural Radiance Fields for Animatable Interacting Hands