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This paper presents a method that can quickly adapt dynamic 3D avatars toarbitrary text descriptions of novel styles. Among existing approaches foravatar stylization, direct optimization methods can produce excellent resultsfor arbitrary styles but they are unpleasantly slow. Furthermore, they requireredoing the optimization process from scratch for every new input. Fastapproximation methods using feed-forward networks trained on a large dataset ofstyle images can generate results for new inputs quickly, but tend not togeneralize well to novel styles and fall short in quality. We thereforeinvestigate a new approach, AlteredAvatar, that combines those two approachesusing the meta-learning framework. In the inner loop, the model learns tooptimize to match a single target style well; while in the outer loop, themodel learns to stylize efficiently across many styles. After training,AlteredAvatar learns an initialization that can quickly adapt within a smallnumber of update steps to a novel style, which can be given using texts, areference image, or a combination of both. We show that AlteredAvatar canachieve a good balance between speed, flexibility and quality, whilemaintaining consistency across a wide range of novel views and facialexpressions.

AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation