论文标题
可控3D人类合成的表面对齐的神经辐射场
Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis
论文作者
论文摘要
我们提出了一种新方法，用于从稀疏的多视图RGB视频中重建可控的隐式3D人类模型。我们的方法定义了网格表面点上的神经场景表示，并距人体网状表面签名。我们确定一个不可区分的问题，当3D空间中的一个点映射到网格上的最近表面点以学习表面对齐的神经场景表示。为了解决这个问题，我们建议使用带有修改后顶点正态的Barycentric插值将点投射到网格表面上。使用ZJU-MOCAP和HUMAN3.6M数据集进行的实验表明，我们的方法在新颖的视图和新颖置式合成中比现有方法更高。我们还证明了我们的方法很容易支持对身体形状和衣服的控制。项目页面：https：//pfnet-research.github.io/surface-aligned-nerf/。
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 page: https://pfnet-research.github.io/surface-aligned-nerf/.
