Your3dEmoji: Creating Personalized Emojis via One-shot 3D-aware Cartoon Avatar Synthesis

Shiyao Xu, Lingzhi Li, Li Shen, Yifang Men, Zhouhui Lian

Abstract: Creating cartoon-style avatars has drawn growing attention recently, however previous methods only learn face cartoonization in the 2D image level. In this paper, we propose a novel 3D generative model to translate a real-world face image into its corresponding 3D avatar with only a single style example provided. To bridge the gap between 2D real faces and 3D cartoon avatars, we leverage the state-of-the-art StyleGAN and its style-mixing property to produce a 2D paired cartoonized face dataset. We then finetune a pretrained 3D GAN with the pair data in a dual-learning mechanism to get the final synthesized 3D avatar. Furthermore, we analyze the latent space of our model, enabling manual control in what degree a style is applied. Our model is 3D-aware in the sense and also able to do attribute editing, such as smile, age, etc directly in the 3D domain. Experimental results demonstrate that our method can produce high-fidelity cartoonized avatars with true-to-life 3D geometry.