使用3D高斯溅射实现极端姿态下的人脸解析高效标签细化
Efficient Label Refinement for Face Parsing Under Extreme Poses Using 3D Gaussian Splatting
AI总结：
本文提出利用3D高斯溅射生成准确分割掩码，通过联合拟合两个模型提升多视角一致性，从而提高极端姿态下的人脸解析精度。
摘要：
在极端视角下的人脸解析仍面临显著挑战，由于此类姿态下的标注数据有限。手动标注成本高且难以大规模实施。我们提出了一种新的标签细化流程，利用3D高斯溅射（3DGS）从噪声多视角预测中生成准确的分割掩码。通过联合拟合两个3DGS模型，一个用于RGB图像，一个用于初始分割图，我们的方法通过共享几何结构实现多视角一致性，从而仅需最小的后处理即可合成姿态多样的训练数据。在该细化数据集上微调人脸解析模型显著提高了对挑战性头部姿态的精度，同时在标准视角上保持强劲性能。广泛的实验，包括人工评估，证明了我们的方法在不需真实3D标注且仅使用少量初始图像的情况下，相比最先进的方法取得了更优的结果。我们的方法为在现实世界中提高人脸解析鲁棒性提供了一种可扩展且有效的方法。
英文摘要：
Accurate face parsing under extreme viewing angles remains a significant challenge due to limited labeled data in such poses. Manual annotation is costly and often impractical at scale. We propose a novel label refinement pipeline that leverages 3D Gaussian Splatting (3DGS) to generate accurate segmentation masks from noisy multiview predictions. By jointly fitting two 3DGS models, one to RGB images and one to their initial segmentation maps, our method enforces multiview consistency through shared geometry, enabling the synthesis of pose-diverse training data with only minimal post-processing. Fine-tuning a face parsing model on this refined dataset significantly improves accuracy on challenging head poses, while maintaining strong performance on standard views. Extensive experiments, including human evaluations, demonstrate that our approach achieves superior results compared to state-of-the-art methods, despite requiring no ground-truth 3D annotations and using only a small set of initial images. Our method offers a scalable and effective solution for improving face parsing robustness in real-world settings.
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