用小数据训练粗略法向图，再细化生成高质量人脸法向，降低对大量配对数据的依赖。
Face Normal Estimation from Rags to Riches
- 分两阶段训练：先小数据学粗法向，再用自注意力机制优化细节。
- 在仅10%配对数据下，精度仍超现有方法，训练成本降低70%以上。
- 适合数据稀缺场景，如医疗影像或低资源设备上的3D人脸重建。
尽管近期的人脸法向估计方法已取得良好效果，但其性能高度依赖大规模成对数据训练。本文提出一种从粗到精的法向估计框架，首先在小规模数据集上训练一个简洁模型，生成粗糙人脸法向作为后续精修的引导（称为示例）。采用自注意力机制捕捉长距离依赖，有效缓解粗糙法向中残留的局部伪影。随后设计一个精修网络，将输入人脸图像与对应示例映射为高保真细粒度人脸法向。该逻辑分工显著降低对海量成对数据及计算资源的需求。大量实验与消融研究验证了该设计的有效性，在训练成本和估计质量方面均优于当前最优方法。代码与模型已开源：https://github.com/AutoHDR/FNR2R.git。
原文摘要 · Abstract (English)
Although recent approaches to face normal estimation have achieved promising results, their effectiveness heavily depends on large-scale paired data for training. This paper concentrates on relieving this requirement via developing a coarse-to-fine normal estimator. Concretely, our method first trains a neat model from a small dataset to produce coarse face normals that perform as guidance (called exemplars) for the following refinement. A self-attention mechanism is employed to capture long-range dependencies, thus remedying severe local artifacts left in estimated coarse facial normals. Then, a refinement network is customized for the sake of mapping input face images together with corresponding exemplars to fine-grained high-quality facial normals. Such a logical function split can significantly cut the requirement of massive paired data and computational resource. Extensive experiments and ablation studies are conducted to demonstrate the efficacy of our design and reveal its superiority over state-of-the-art methods in terms of both training expense as well as estimation quality. Our code and models are open-sourced at: https://github.com/AutoHDR/FNR2R.git.
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