FreeUV：基于交叉组装推理策略的无真实标签真实感面部UV纹理恢复
FreeUV: Ground-Truth-Free Realistic Facial UV Texture Recovery via Cross-Assembly Inference Strategy
AI总结：
针对单视图2D图像恢复3D面部纹理的数据限制与细节挑战，提出无真实标签框架FreeUV，结合预训练稳定扩散模型与交叉组装推理策略，性能超越现有最优方法，还支持多种新应用，提供可扩展的高保真纹理生成方案。
摘要：
从单视图2D图像中恢复高质量3D面部纹理是一项具有挑战性的任务，尤其是在数据有限以及存在妆容、皱纹、遮挡等复杂面部细节的约束下。本文提出了FreeUV，一种全新的无真实标签UV纹理恢复框架，无需标注或合成的UV数据。FreeUV利用预训练的stable diffusion模型（稳定扩散模型），结合交叉组装推理策略实现这一目标。在FreeUV中，独立训练不同的网络分别聚焦真实感外观和结构一致性，推理阶段将这些网络结合以生成连贯的纹理。该方法能准确捕捉精细的面部特征，在多样姿态和遮挡场景下均表现出鲁棒性能。大量实验验证了FreeUV的有效性，其结果在定量和定性指标上均超越了现有最优方法。此外，FreeUV还支持局部编辑、面部特征插值和多视图纹理恢复等新应用。通过降低数据需求，FreeUV为生成适用于真实场景的高保真3D面部纹理提供了可扩展的解决方案。
英文摘要：
Recovering high-quality 3D facial textures from single-view 2D images is a challenging task, especially under constraints of limited data and complex facial details such as makeup, wrinkles, and occlusions. In this paper, we introduce FreeUV, a novel ground-truth-free UV texture recovery framework that eliminates the need for annotated or synthetic UV data. FreeUV leverages pre-trained stable diffusion model alongside a Cross-Assembly inference strategy to fulfill this objective. In FreeUV, separate networks are trained independently to focus on realistic appearance and structural consistency, and these networks are combined during inference to generate coherent textures. Our approach accurately captures intricate facial features and demonstrates robust performance across diverse poses and occlusions. Extensive experiments validate FreeUV's effectiveness, with results surpassing state-of-the-art methods in both quantitative and qualitative metrics. Additionally, FreeUV enables new applications, including local editing, facial feature interpolation, and multi-view texture recovery. By reducing data requirements, FreeUV offers a scalable solution for generating high-fidelity 3D facial textures suitable for real-world scenarios.
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。
