Photo-realistic re-rendering of a human from a single image with explicit
control over body pose, shape and appearance enables a wide range of
applications, such as human appearance transfer, virtual try-on, motion
imitation, and novel view synthesis. While significant progress has been made
in this direction using learning-based image generation tools, such as GANs,
existing approaches yield noticeable artefacts such as blurring of fine
details, unrealistic distortions of the body parts and garments as well as
severe changes of the textures. We, therefore, propose a new method for
synthesising photo-realistic human images with explicit control over pose and
part-based appearance, i.e., StylePoseGAN, where we extend a non-controllable
generator to accept conditioning of pose and appearance separately. Our network
can be trained in a fully supervised way with human images to disentangle pose,
appearance and body parts, and it significantly outperforms existing single
image re-rendering methods. Our disentangled representation opens up further
applications such as garment transfer, motion transfer, virtual try-on, head
(identity) swap and appearance interpolation. StylePoseGAN achieves
state-of-the-art image generation fidelity on common perceptual metrics
compared to the current best-performing methods and convinces in a
comprehensive user study.

本文提出了一种新方法，称为 StylePoseGAN，用于合成具有姿势和部位外观显式控制的照片真实感的人类图像，并能够将其用于虚拟换装、动作模仿和新视角合成等应用中。

单目视角下的人类图像综合的风格和姿态控制

Style and Pose Control for Image Synthesis of Humans from a Single  Monocular View

We present a framework for training GANs with explicit control over generated
images. We are able to control the generated image by settings exact attributes
such as age, pose, expression, etc. Most approaches for editing GAN-generated
images achieve partial control by leveraging the latent space disentanglement
properties, obtained implicitly after standard GAN training. Such methods are
able to change the relative intensity of certain attributes, but not explicitly
set their values. Recently proposed methods, designed for explicit control over
human faces, harness morphable 3D face models to allow fine-grained control
capabilities in GANs. Unlike these methods, our control is not constrained to
morphable 3D face model parameters and is extendable beyond the domain of human
faces. Using contrastive learning, we obtain GANs with an explicitly
disentangled latent space. This disentanglement is utilized to train
control-encoders mapping human-interpretable inputs to suitable latent vectors,
thus allowing explicit control. In the domain of human faces we demonstrate
control over identity, age, pose, expression, hair color and illumination. We
also demonstrate control capabilities of our framework in the domains of
painted portraits and dog image generation. We demonstrate that our approach
achieves state-of-the-art performance both qualitatively and quantitatively.

我们提出了一个具有明确控制 GAN 生成的图像的框架，借助于对抗性训练（GANs）、明确控制的潜在空间以及对人类的符号解释模型进行了对生成图像的控制，可扩展到人类脸谱以外的领域，实现了身份、年龄、姿态、表情、头发色彩和光照方面的控制，并且在画像和狗图像生成的领域中都展示了控制能力，同时表现出优良的定量和定性性能。