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Dynamic Neural Radiance Fields (NeRFs) achieve remarkable visual quality whensynthesizing novel views of time-evolving 3D scenes. However, the commonreliance on backward deformation fields makes reanimation of the capturedobject poses challenging. Moreover, the state of the art dynamic models areoften limited by low visual fidelity, long reconstruction time or specificityto narrow application domains. In this paper, we present a novel methodutilizing a point-based representation and Linear Blend Skinning (LBS) tojointly learn a Dynamic NeRF and an associated skeletal model from even sparsemulti-view video. Our forward-warping approach achieves state-of-the-art visualfidelity when synthesizing novel views and poses while significantly reducingthe necessary learning time when compared to existing work. We demonstrate theversatility of our representation on a variety of articulated objects fromcommon datasets and obtain reposable 3D reconstructions without the need ofobject-specific skeletal templates. Code will be made available athttps://github.com/lukasuz/Articulated-Point-NeRF.

Template-free Articulated Neural Point Clouds for Reposable View Synthesis