Face Normal Estimation from Rags to Riches

2026-01-05

arXiv.org

AI Summary

This work addresses the heavy reliance of facial normal estimation on large-scale paired training data by proposing a coarse-to-fine two-stage approach. In the first stage, a base model trained on a small dataset generates guiding exemplars; in the second stage, these exemplars are jointly processed with the input image through a customized refinement network enhanced with self-attention mechanisms to produce high-quality normals. By decomposing the task into logically distinct functional stages, the method substantially reduces demands on both data volume and computational resources while effectively mitigating local artifacts. Experiments demonstrate that the proposed approach outperforms current state-of-the-art methods in both training efficiency and estimation accuracy, and ablation studies confirm the contribution of each component to the overall performance.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Multimodal Learning
• Natural Language Processing: (Large) Language Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• face normal estimation
• paired data
• data efficiency
• coarse-to-fine estimation
• training data requirement

Innovation

• coarse-to-fine estimation
• self-attention mechanism
• face normal estimation
• data-efficient learning
• exemplar-guided refinement