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While deep learning reshaped the classical motion capture pipeline withfeed-forward networks, generative models are required to recover fine alignmentvia iterative refinement. Unfortunately, the existing models are usuallyhand-crafted or learned in controlled conditions, only applicable to limiteddomains. We propose a method to learn a generative neural body model fromunlabelled monocular videos by extending Neural Radiance Fields (NeRFs). Weequip them with a skeleton to apply to time-varying and articulated motion. Akey insight is that implicit models require the inverse of the forwardkinematics used in explicit surface models. Our reparameterization definesspatial latent variables relative to the pose of body parts and therebyovercomes ill-posed inverse operations with an overparameterization. Thisenables learning volumetric body shape and appearance from scratch whilejointly refining the articulated pose; all without ground truth labels forappearance, pose, or 3D shape on the input videos. When used fornovel-view-synthesis and motion capture, our neural model improves accuracy ondiverse datasets. Project website: https://lemonatsu.github.io/anerf/ .

A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose