TIFace: Improving Facial Reconstruction through Tensorial Radiance
  Fields and Implicit Surfaces
Ruijie Zhu Jiahao Chang Ziyang Song Jiahuan Yu Tianzhu Zhang
This report describes the solution that secured the first place in the "View
Synthesis Challenge for Human Heads (VSCHH)" at the ICCV 2023 workshop. Given
the sparse view images of human heads, the objective of this challenge is to
synthesize images from novel viewpoints. Due to the complexity of textures on
the face and the impact of lighting, the baseline method TensoRF yields results
with significant artifacts, seriously affecting facial reconstruction. To
address this issue, we propose TI-Face, which improves facial reconstruction
through tensorial radiance fields (T-Face) and implicit surfaces (I-Face),
respectively. Specifically, we employ an SAM-based approach to obtain the
foreground mask, thereby filtering out intense lighting in the background.
Additionally, we design mask-based constraints and sparsity constraints to
eliminate rendering artifacts effectively. The experimental results demonstrate
the effectiveness of the proposed improvements and superior performance of our
method on face reconstruction. The code will be available at
https://github.com/RuijieZhu94/TI-Face.