解决遮挡下3D人脸重建的细节丢失问题，提升真实感。
Learning Contour-Guided 3D Face Reconstruction with Occlusions
- 引入中层形状精修机制，增强遮挡区域结构鲁棒性。
- 在有遮挡时仍能生成符合物理规律的面部细节。
- 适合需要高精度人脸重建的虚拟现实与逆向工程场景。
近年来，基于深度学习的3D人脸重建方法在质量和效率方面取得了显著进展。然而，这些技术在处理遮挡场景时仍面临挑战，难以捕捉精细的面部几何细节。受GANs和法线贴图原理启发，我们提出了一种新方法，旨在即使在存在遮挡的情况下也能实现完整的3D人脸重建。在保持整体形状稳定性的基础上，我们引入中层形状精修机制对基础结构进行优化，并展示该方法如何有效生成被遮挡面部区域的合理细节。通过大量实例验证，本框架在生成逼真结果方面优于传统方法，尤其在传统技术易失效的复杂遮挡场景中表现突出。我们在通用3D人脸重建任务上进行了广泛实验，充分证明了该方法相比人工遮挡去除策略具有更强的适应性与鲁棒性。
原文摘要 · Abstract (English)
Recently, deep learning-based 3D face reconstruction methods have demonstrated promising advancements in terms of quality and efficiency. Nevertheless, these techniques face challenges in effectively handling occluded scenes and fail to capture intricate geometric facial details. Inspired by the principles of GANs and bump mapping, we have successfully addressed these issues. Our approach aims to deliver comprehensive 3D facial reconstructions, even in the presence of occlusions.While maintaining the overall shape's robustness, we introduce a mid-level shape refinement to the fundamental structure. Furthermore, we illustrate how our method adeptly extends to generate plausible details for obscured facial regions. We offer numerous examples that showcase the effectiveness of our framework in producing realistic results, where traditional methods often struggle. To substantiate the superior adaptability of our approach, we have conducted extensive experiments in the context of general 3D face reconstruction tasks, serving as concrete evidence of its regulatory prowess compared to manual occlusion removal methods.
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