AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation
This work addresses the need for flexible and controllable audio-driven animation in applications like facial motion editing or face reenactment, representing an incremental advancement in the field.
The paper tackles the problem of generating photorealistic portrait animation from audio and a reference image, achieving high-quality results with improved facial naturalness, pose diversity, and visual quality.
In this study, we propose AniPortrait, a novel framework for generating high-quality animation driven by audio and a reference portrait image. Our methodology is divided into two stages. Initially, we extract 3D intermediate representations from audio and project them into a sequence of 2D facial landmarks. Subsequently, we employ a robust diffusion model, coupled with a motion module, to convert the landmark sequence into photorealistic and temporally consistent portrait animation. Experimental results demonstrate the superiority of AniPortrait in terms of facial naturalness, pose diversity, and visual quality, thereby offering an enhanced perceptual experience. Moreover, our methodology exhibits considerable potential in terms of flexibility and controllability, which can be effectively applied in areas such as facial motion editing or face reenactment. We release code and model weights at https://github.com/scutzzj/AniPortrait