# Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision

arXiv: https://arxiv.org/abs/2008.06843 · HTML: https://mlsanity.com/paper/2008.06843/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2020.

## Built on by (most cited first, 12 of 12)

- [CCVS: Context-aware Controllable Video Synthesis](https://mlsanity.com/paper/2107.08037/) (2021)
- [Structure-Aware Motion Transfer with Deformable Anchor Model](https://mlsanity.com/paper/2204.05018/) (2022)
- [NIS-SLAM: Neural Implicit Semantic RGB-D SLAM for 3D Consistent Scene Understanding](https://mlsanity.com/paper/2407.20853/) (2024)
- [Motion Transformer for Unsupervised Image Animation](https://mlsanity.com/paper/2209.14024/) (2022)
- [Learning Motion Refinement for Unsupervised Face Animation](https://mlsanity.com/paper/2310.13912/) (2023)
- [Heterogeneous Face Frontalization via Domain Agnostic Learning](https://mlsanity.com/paper/2107.08311/) (2021)
- [Learning Real-World Image De-Weathering with Imperfect Supervision](https://mlsanity.com/paper/2310.14958/) (2023)
- [Information Maximization for Extreme Pose Face Recognition](https://mlsanity.com/paper/2209.03456/) (2022)
- [UNITY: Attention Flow Networks for Adaptive Conditioning in Diffusion](https://mlsanity.com/paper/2606.20971/) (2026)
- [Exploring Biases and Prejudice of Facial Synthesis via Semantic Latent Space](https://mlsanity.com/paper/2108.10265/) (2021)
- [Pseudo-Label Guided Real-World Image De-weathering: A Learning Framework with Imperfect Supervision](https://mlsanity.com/paper/2504.09949/) (2025)
- [Towards Large-Scale Pose-Invariant Face Recognition Using Face Defrontalization](https://mlsanity.com/paper/2506.04496/) (2025)

## Abstract (arXiv)

Despite recent advances in deep learning-based face frontalization methods, photo-realistic and illumination preserving frontal face synthesis is still challenging due to large pose and illumination discrepancy during training. We propose a novel Flow-based Feature Warping Model (FFWM) which can learn to synthesize photo-realistic and illumination preserving frontal images with illumination inconsistent supervision. Specifically, an Illumination Preserving Module (IPM) is proposed to learn illumination preserving image synthesis from illumination inconsistent image pairs. IPM includes two pathways which collaborate to ensure the synthesized frontal images are illumination preserving and with fine details. Moreover, a Warp Attention Module (WAM) is introduced to reduce the pose discrepancy in the feature level, and hence to synthesize frontal images more effectively and preserve more details of profile images. The attention mechanism in WAM helps reduce the artifacts caused by the displacements between the profile and the frontal images. Quantitative and qualitative experimental results show that our FFWM can synthesize photo-realistic and illumination preserving frontal images and performs favorably against the state-of-the-art results.