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We propose Neural Deformable Fields (NDF), a new representation for dynamichuman digitization from a multi-view video. Recent works proposed to representa dynamic human body with shared canonical neural radiance fields which linksto the observation space with deformation fields estimations. However, thelearned canonical representation is static and the current design of thedeformation fields is not able to represent large movements or detailedgeometry changes. In this paper, we propose to learn a neural deformable fieldwrapped around a fitted parametric body model to represent the dynamic human.The NDF is spatially aligned by the underlying reference surface. A neuralnetwork is then learned to map pose to the dynamics of NDF. The proposed NDFrepresentation can synthesize the digitized performer with novel views andnovel poses with a detailed and reasonable dynamic appearance. Experiments showthat our method significantly outperforms recent human synthesis methods.

NDF: Neural Deformable Fields for Dynamic Human Modelling