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。

GAN-Control: 明确可控的GANs