This paper presents an innovative approach to achieve face cartoonisation while preserving the original identity and accommodating various poses. Unlike previous methods in this field that relied on conditional-GANs, which posed challenges related to dataset requirements and pose training, our approach leverages the expressive latent space of StyleGAN. We achieve this by introducing an encoder that captures both pose and identity information from images and generates a corresponding embedding within the StyleGAN latent space. By subsequently passing this embedding through a pre-trained generator, we obtain the desired cartoonised output. While many other approaches based on StyleGAN necessitate a dedicated and fine-tuned StyleGAN model, our method stands out by utilizing an already-trained StyleGAN designed to produce realistic facial images. We show by extensive experimentation how our encoder adapts the StyleGAN output to better preserve identity when the objective is cartoonisation.

该论文提出了一种创新的方法，可以实现面部卡通化，同时保留原始身份并适应各种姿势。与以前依赖条件生成对抗网络 (conditional-GANs) 的方法不同，我们的方法利用了 StyleGAN 的表达潜在空间。我们通过引入一个编码器来从图像中捕捉姿势和身份信息，并生成对应的 StyleGAN 潜在空间中的嵌入。然后，通过一个预训练的生成器将这个嵌入传递，从而得到所需的卡通化输出。与许多其他基于 StyleGAN 的方法需要一个专门调整的、经过精细调整的 StyleGAN 模型不同，我们的方法通过利用已经训练好的 StyleGAN 来产生逼真的面部图像。我们通过广泛的实验表明，当目标是卡通化时，我们的编码器能够适应 StyleGAN 的输出并更好地保留身份信息。

使用StyleGAN进行各种姿势的面部卡通化