We propose a new method for reconstructing controllable implicit 3D human
models from sparse multi-view RGB videos. Our method defines the neural scene
representation on the mesh surface points and signed distances from the surface
of a human body mesh. We identify an indistinguishability issue that arises
when a point in 3D space is mapped to its nearest surface point on a mesh for
learning surface-aligned neural scene representation. To address this issue, we
propose projecting a point onto a mesh surface using a barycentric
interpolation with modified vertex normals. Experiments with the ZJU-MoCap and
Human3.6M datasets show that our approach achieves a higher quality in a
novel-view and novel-pose synthesis than existing methods. We also demonstrate
that our method easily supports the control of body shape and clothes. Project
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本研究提出了一种从稀疏的多视角 RGB 视频中重建可控的隐式 3D 人体模型的新方法，使用网格表面点和身体网格的符号距离来定义神经场景表示，并通过改进的顶点法线的重心插值将点投影到网格表面上，实现了更高质量的新视角和新姿势合成，同时支持容易控制的身体形状和服装。