Recently, implicit neural representation has been widely used to generate
animatable human avatars. However, the materials and geometry of those
representations are coupled in the neural network and hard to edit, which
hinders their application in traditional graphics engines. We present a
framework for acquiring human avatars that are attached with high-resolution
physically-based material textures and triangular mesh from monocular video.
Our method introduces a novel information fusion strategy to combine the
information from the monocular video and synthesize virtual multi-view images
to tackle the sparsity of the input view. We reconstruct humans as deformable
neural implicit surfaces and extract triangle mesh in a well-behaved pose as
the initial mesh of the next stage. In addition, we introduce an approach to
correct the bias for the boundary and size of the coarse mesh extracted.
Finally, we adapt prior knowledge of the latent diffusion model at
super-resolution in multi-view to distill the decomposed texture. Experiments
show that our approach outperforms previous representations in terms of high
fidelity, and this explicit result supports deployment on common renderers.

通过单目视频实现高分辨率物理材质纹理和三角网格的人体模型的获取并结合多视角图像合成的信息融合策略，通过神经隐式表达生成可编辑的变形人体模型。