EasyAnim: 3D facial animation from in-the-wild videos for avatars with customized riggings

3D facial animation of digital avatars driven by RGB videos has extensive applications. However, the practical implementation encounters a significant challenge due to the various identities and environments of in-the-wild videos and varying rigging designs. The traditional industry pipeline necessitates a labor-intensive alignment process to ensure compatibility, while the recent novel methods are constrained to a specific rigging standard or require additional labor on actor videos, making them difficult to apply to customized riggings and in-the-wild videos. To make the task easy and convenient, we introduce EasyAnim, which utilizes abundant 2D videos to learn an aligned implicit motion flow unsupervisedly and maps it to various rigging parameters in a generalized manner. A novel framework with self- and cross- reconstruction constraints is proposed to ensure the alignment of avatar and human actor domains. Extensive experiments demonstrate that EasyAnim generates comparable or even better results with no additional constraints and labor.