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
this https URL

通过使用张量辐射场（T-Face）和隐式表面（I-Face）的方法，我们提出了 TI-Face 来改进面部重建，包括通过强调前景掩码、设计基于掩码和稀疏性约束来有效消除渲染伪影，实验结果表明，提出的改进方法在面部重建上表现出了显著的效果和卓越的性能。

TIFace：通过张量辐射场和隐式曲面改善面部重建

TIFace: Improving Facial Reconstruction through Tensorial Radiance  Fields and Implicit Surfaces

Person detection is a key problem for many computer vision tasks. While face
detection has reached maturity, detecting people under a full variation of
camera view-points, human poses, lighting conditions and occlusions is still a
difficult challenge. In this work we focus on detecting human heads in natural
scenes. Starting from the recent local R-CNN object detector, we extend it with
two types of contextual cues. First, we leverage person-scene relations and
propose a Global CNN model trained to predict positions and scales of heads
directly from the full image. Second, we explicitly model pairwise relations
among objects and train a Pairwise CNN model using a structured-output
surrogate loss. The Local, Global and Pairwise models are combined into a joint
CNN framework. To train and test our full model, we introduce a large dataset
composed of 369,846 human heads annotated in 224,740 movie frames. We evaluate
our method and demonstrate improvements of person head detection against
several recent baselines in three datasets. We also show improvements of the
detection speed provided by our model.

本研究利用全局 CNN 模型和成对 CNN 模型来检测自然场景下的人头部位，通过引入新的数据集进行测试，结果显著提高了人头部位检测的准确性，并优化了检测速度。