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we present snarf differentiable forward skinning for animating non-rigid neural implicit shapes neural implicit representations have emerged as a powerful tool to capture geometry at high fidelity with flexible topology however most approaches focus on static scenes only in this work we propose to learn a skinning model jointly with the implicit shape enabling animation of neural implicit avatars the input to our method is a set of post 3d meshes in combination with the corresponding object poses given this input we jointly learn a shape and skinning weight representation in canonical space this allows our model to reconstruct continuous implicit surfaces for unseen poses even in cases where the target pose is far from the poses observed during training but how can we deform a continuous function in a differentiable way existing methods use backward scanning and learn a weight field conditioned on the deformed point and the object pose in contrast in this work we propose a forward scan
