Caricature, a type of exaggerated artistic portrait, amplifies the distinctive, yet nuanced traits of human faces. This task is typically left to artists, as it has proven difficult to capture subjects' unique characteristics well using automated methods. Recent development of deep end-to-end methods has achieved promising results in capturing style and higher-level exaggerations. However, a key part of caricatures, face warping, has remained challenging for these systems. In this work, we propose AutoToon, the first supervised deep learning method that yields high-quality warps for the warping component of caricatures. Completely disentangled from style, it can be paired with any stylization method to create diverse caricatures. In contrast to prior art, we leverage an SENet and spatial transformer module and train directly on artist warping fields, applying losses both prior to and after warping. As shown by our user studies, we achieve appealing exaggerations that amplify distinguishing features of the face while preserving facial detail.

本文介绍了AutoToon，第一个通过深度学习提供高质量脸部变形的方法，这是卡通画的关键部分。与之前的方法不同，AutoToon利用SENet和空间变换模块，直接在艺术家变形场上训练，通过前后损失来增强差异特征并保留面部细节。

AutoToon: 自动几何扭曲用于生成卡通脸部