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We introduce neural blend skinning models that allow for differentiable and invertible articulated deformations. When combined with canonical embeddings, such models allow us to establish dense correspondences across videos that can be self-supervised with cycle consistency. On real and synthetic datasets, BANMo shows higher-fidelity 3D reconstructions than prior works for humans and animals, with the ability to render realistic images from novel viewpoints and poses. Animatable models of 3D objects from casually collected videos. A representative application is content creation for virtual and augmented reality, where the goal is to 3D-ify images and videos captured by users for consumption in a 3D space or creating animatable assets such as avatars. However, these approaches do not generalize to deformable objects such as family members, friends or pets, which are often the focus of user content. We are thus interested in reconstructing 3D deformable objects from casually collected videos. However, individual videos may not contain sufficient information to obtain good reconstruction of a given subject.




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