Denoising Diffusion Models (DDM) are emerging as the cutting-edge technology
in the realm of deep generative modeling, challenging the dominance of
Generative Adversarial Networks. However, effectively exploring the latent
space's semantics and identifying compelling trajectories for manipulating and
editing important attributes of the generated samples remains challenging,
primarily due to the high-dimensional nature of the latent space. In this
study, we specifically concentrate on face rotation, which is known to be one
of the most intricate editing operations. By leveraging a recent embedding
technique for Denoising Diffusion Implicit Models (DDIM), we achieve, in many
cases, noteworthy manipulations encompassing a wide rotation angle of $\pm
30^o$, preserving the distinct characteristics of the individual. Our
methodology exploits the computation of trajectories approximating clouds of
latent representations of dataset samples with different yaw rotations through
linear regression. Specific trajectories are obtained by restricting the
analysis to subsets of data sharing significant attributes with the source
image. One of these attributes is the light provenance: a byproduct of our
research is a labeling of CelebA, categorizing images into three major groups
based on the illumination direction: left, center, and right.

利用噪声扩散模型，通过嵌入技术在人脸旋转方面实现了显著的操作和编辑，保留了个体的显著特征。

去噪扩散模型中的头部旋转

Head Rotation in Denoising Diffusion Models

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.

本文提出了一种基于 3D 面部建模和高分辨率 GAN 的无监督框架，旋转面部并在 2D 平面上重新渲染以增强数据，并实现现实世界中单视角图像集合下的逆向旋转合成，模型表现出卓越的合成质量和身份保护性能，可以作为现代人脸识别系统的数据增强引擎。