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We propose a new method for reconstructing controllable implicit 3D humanmodels from sparse multi-view RGB videos. Our method defines the neural scenerepresentation on the mesh surface points and signed distances from the surfaceof a human body mesh. We identify an indistinguishability issue that ariseswhen a point in 3D space is mapped to its nearest surface point on a mesh forlearning surface-aligned neural scene representation. To address this issue, wepropose projecting a point onto a mesh surface using a barycentricinterpolation with modified vertex normals. Experiments with the ZJU-MoCap andHuman3.6M datasets show that our approach achieves a higher quality in anovel-view and novel-pose synthesis than existing methods. We also demonstratethat our method easily supports the control of body shape and clothes. Projectpage: https://pfnet-research.github.io/surface-aligned-nerf/.

Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis