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Recently, many works have been proposed to utilize the neural radiance fieldfor novel view synthesis of human performers. However, most of these methodsrequire hours of training, making them difficult for practical use. To addressthis challenging problem, we propose IntrinsicNGP, which can train from scratchand achieve high-fidelity results in few minutes with videos of a humanperformer. To achieve this target, we introduce a continuous and optimizableintrinsic coordinate rather than the original explicit Euclidean coordinate inthe hash encoding module of instant-NGP. With this novel intrinsic coordinate,IntrinsicNGP can aggregate inter-frame information for dynamic objects with thehelp of proxy geometry shapes. Moreover, the results trained with the givenrough geometry shapes can be further refined with an optimizable offset fieldbased on the intrinsic coordinate.Extensive experimental results on severaldatasets demonstrate the effectiveness and efficiency of IntrinsicNGP. We alsoillustrate our approach's ability to edit the shape of reconstructed subjects.

IntrinsicNGP: Intrinsic Coordinate based Hash Encoding for Human NeRF