Abstract
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 this http URL 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.
Abstract (translated)
最近，基于深度学习的三维人脸重建方法在质量和效率方面取得了显著进展。然而，这些技术在处理被遮挡场景时仍然面临挑战，并且无法捕捉到复杂的几何面部细节。受GAN（生成对抗网络）和凹凸贴图原理的启发，我们成功地解决了这些问题。我们的方法旨在提供全面的三维面部重建，在存在遮挡的情况下也能保持整体形状的稳健性，并在此基础上引入了中层形状细化技术来改进基础结构。此外，我们展示了如何利用该方法为被遮盖的面部区域生成合理的细节。通过大量实例，我们证明了框架在产生逼真结果方面的有效性，而传统方法通常难以达到这一效果。 为了证实我们的方法具有更优越的适应性，我们在一般的三维人脸重建任务中进行了广泛的实验，并将其实验结果作为与手动遮挡去除方法相比，其调控能力更为卓越的具体证据。
URL
https://arxiv.org/abs/2503.12494
