解决遮挡下人脸三维重建的几何细节丢失问题
Geometry-Aware Face Reconstruction Under Occluded Scenes
- 结合GAN与凹凸贴图思想，分层优化面部几何结构
- 在遮挡区域仍能生成逼真细节，提升重建完整性
- 适合需要高精度人脸重建的应用场景
近年来，基于深度学习的3D人脸重建方法在质量和效率上取得了显著进展。然而，这些技术在处理遮挡场景时仍存在挑战，难以捕捉复杂的面部几何细节。受GAN和凹凸贴图原理启发，本文提出一种新方法，在保持整体形状鲁棒性的同时，引入中层形状精修机制，有效提升基础结构表现。此外，该方法可自然延伸至生成被遮挡面部区域的合理细节。通过大量实验验证，其在通用3D人脸重建任务中展现出优于传统手动去遮挡方法的适应能力，充分证明了框架的泛化性能与实用性。
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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