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We present a novel paradigm of building an animatable 3D human representationfrom a monocular video input, such that it can be rendered in any unseen posesand views. Our method is based on a dynamic Neural Radiance Field (NeRF) riggedby a mesh-based parametric 3D human model serving as a geometry proxy. Previousmethods usually rely on multi-view videos or accurate 3D geometry informationas additional inputs; besides, most methods suffer from degraded quality whengeneralized to unseen poses. We identify that the key to generalization is agood input embedding for querying dynamic NeRF: A good input embedding shoulddefine an injective mapping in the full volumetric space, guided by surfacemesh deformation under pose variation. Based on this observation, we propose toembed the input query with its relationship to local surface regions spanned bya set of geodesic nearest neighbors on mesh vertices. By including bothposition and relative distance information, our embedding defines adistance-preserved deformation mapping and generalizes well to unseen poses. Toreduce the dependency on additional inputs, we first initialize per-frame 3Dmeshes using off-the-shelf tools and then propose a pipeline to jointlyoptimize NeRF and refine the initial mesh. Extensive experiments show ourmethod can synthesize plausible human rendering results under unseen poses andviews.

Neural Capture of Animatable 3D Human from Monocular Video