AlteredAvatar：使用快速风格适应对动态3D头像进行风格化

摘要

本文提出了一种方法，可以快速将动态3D头像调整到任意文本描述的新颖风格。在现有的头像风格化方法中，直接优化方法可以为任意风格产生出色的结果，但速度慢且需要针对每个新输入重新进行优化过程。使用在大量风格图像数据集上训练的前馈网络的快速近似方法可以快速生成新输入的结果，但往往不太适用于新颖风格，并且在质量上表现不佳。因此，我们研究了一种新方法，AlteredAvatar，它结合了这两种方法，使用元学习框架。在内循环中，模型学习优化以匹配单个目标风格；而在外循环中，模型学习有效地跨多种风格进行风格化。训练后，AlteredAvatar学习到一种初始化方法，可以在少量更新步骤内快速适应新颖风格，这些风格可以通过文本、参考图像或二者的组合给出。我们展示AlteredAvatar可以在速度、灵活性和质量之间取得良好平衡，同时在广泛的新视角和面部表情中保持一致性。

English

This paper presents a method that can quickly adapt dynamic 3D avatars to arbitrary text descriptions of novel styles. Among existing approaches for avatar stylization, direct optimization methods can produce excellent results for arbitrary styles but they are unpleasantly slow. Furthermore, they require redoing the optimization process from scratch for every new input. Fast approximation methods using feed-forward networks trained on a large dataset of style images can generate results for new inputs quickly, but tend not to generalize well to novel styles and fall short in quality. We therefore investigate a new approach, AlteredAvatar, that combines those two approaches using the meta-learning framework. In the inner loop, the model learns to optimize to match a single target style well; while in the outer loop, the model learns to stylize efficiently across many styles. After training, AlteredAvatar learns an initialization that can quickly adapt within a small number of update steps to a novel style, which can be given using texts, a reference image, or a combination of both. We show that AlteredAvatar can achieve a good balance between speed, flexibility and quality, while maintaining consistency across a wide range of novel views and facial expressions.

AlteredAvatar：使用快速风格适应对动态3D头像进行风格化

AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation

摘要

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