Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images

TL;DR

This paper introduces an unsupervised method for photorealistic face rotation from single images, utilizing 3D face modeling and GANs to generate high-quality rotated faces without paired training data.

Contribution

It proposes a novel rotate-and-render framework that enables high-quality face rotation using only single-view images, overcoming the need for multi-view datasets and improving in-the-wild face synthesis.

Findings

Outperforms state-of-the-art methods in synthesis quality and identity preservation.

Effective as a data augmentation tool for face recognition systems.

Works well across diverse poses and domains without paired data.

Abstract

Though face rotation has achieved rapid progress in recent years, the lack of high-quality paired training data remains a great hurdle for existing methods. The current generative models heavily rely on datasets with multi-view images of the same person. Thus, their generated results are restricted by the scale and domain of the data source. To overcome these challenges, we propose a novel unsupervised framework that can synthesize photo-realistic rotated faces using only single-view image collections in the wild. Our key insight is that rotating faces in the 3D space back and forth, and re-rendering them to the 2D plane can serve as a strong self-supervision. We leverage the recent advances in 3D face modeling and high-resolution GAN to constitute our building blocks. Since the 3D rotation-and-render on faces can be applied to arbitrary angles without losing details, our approach is extremely suitable for in-the-wild scenarios (i.e. no paired data are available), where existing methods fall short. Extensive experiments demonstrate that our approach has superior synthesis quality as well as identity preservation over the state-of-the-art methods, across a wide range of poses and domains. Furthermore, we validate that our rotate-and-render framework naturally can act as an effective data augmentation engine for boosting modern face recognition systems even on strong baseline models.