Geometry-Aware Face Reconstruction Under Occluded Scenes
This work addresses the challenge of reconstructing detailed 3D faces from occluded images, which is important for applications like computer vision and graphics, but it appears incremental as it builds on GANs and bump mapping principles.
The paper tackles the problem of 3D face reconstruction in occluded scenes, where existing deep learning methods struggle with occlusions and geometric details, and demonstrates that their approach produces realistic results with superior adaptability compared to traditional methods.
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.