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.

翻译：本报告描述了在ICCV 2023研讨会举办的"人头视角合成挑战赛"中获得第一名的解决方案。针对稀疏视角下的人头图像，该挑战的目标是从新视角合成图像。由于面部纹理的复杂性及光照影响，基线方法TensoRF生成的图像存在明显伪影，严重影响了面部重建效果。为解决此问题，我们提出TI-Face方法，分别通过张量辐射场（T-Face）与隐式曲面（I-Face）改进了面部重建。具体而言，我们采用基于SAM的方法获取前景掩码，从而过滤背景中的强光照；此外，设计了基于掩码的约束和稀疏性约束以有效消除渲染伪影。实验结果表明了所提改进的有效性，以及本方法在人脸重建中的优越性能。代码将提供于https://github.com/RuijieZhu94/TI-Face。