遮挡场景下的几何感知人脸重建
Geometry-Aware Face Reconstruction Under Occluded Scenes
AI总结：
针对遮挡场景下3D人脸重建难以处理遮挡并恢复精细几何的问题，提出融合GANs与凹凸映射思想的几何感知框架，通过中级形状细化和遮挡区域细节生成提升真实感与适应性。
摘要：
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展现出可喜的进展。然而，这些技术在有效处理遮挡场景方面面临挑战，并且无法捕捉复杂的面部几何细节。受GANs（生成对抗网络）和凹凸映射原理的启发，我们成功解决了这些问题。我们的方法旨在即使在存在遮挡的情况下也能提供全面的3D面部重建。在保持整体形状鲁棒性的同时，我们在基础结构中引入了中级形状细化。此外，我们说明了我们的方法如何熟练地扩展，为被遮挡的面部区域生成合理的细节。我们提供了大量示例，展示了我们的框架在生成真实结果方面的有效性，而传统方法往往难以做到这一点。为了证实我们方法的卓越适应性，我们在通用3D人脸重建任务背景下进行了大量实验，与人工遮挡去除方法相比，这些实验具体证明了其调控能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
