学习具有遮挡的轮廓引导3D人脸重建
Learning Contour-Guided 3D Face Reconstruction with Occlusions
AI总结：
针对遮挡场景下3D人脸重建难以捕捉精细几何细节的问题，提出受GANs和凹凸映射启发的轮廓引导方法，通过中级形状细化及遮挡区域细节生成，实现鲁棒真实的重建效果。
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良好的发展前景。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人脸几何细节。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人脸重建。在保持整体形状鲁棒性的同时，我们对基础结构引入了中级形状细化。此外，我们说明了我们的方法如何巧妙地扩展，为被遮挡的人脸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产生真实结果方面的有效性，而传统方法往往难以做到。为了证实我们方法的优越适应性，我们在一般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，这些实验成为其调控能力的具体证据。
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。
