FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio

Chao Xu, Yang Liu, Jiazheng Xing, Weida Wang, Mingze Sun, Jun Dan, Tianxin Huang, Siyuan Li, Zhi-Qi Cheng, Ying Tai, Baigui Sun; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 1292-1302

In this paper we abstract the process of people hearing speech extracting meaningful cues and creating various dynamically audio-consistent talking faces termed Listening and Imagining into the task of high-fidelity diverse talking faces generation from a single audio. Specifically it involves two critical challenges: one is to effectively decouple identity content and emotion from entangled audio and the other is to maintain intra-video diversity and inter-video consistency. To tackle the issues we first dig out the intricate relationships among facial factors and simplify the decoupling process tailoring a Progressive Audio Disentanglement for accurate facial geometry and semantics learning where each stage incorporates a customized training module responsible for a specific factor. Secondly to achieve visually diverse and audio-synchronized animation solely from input audio within a single model we introduce the Controllable Coherent Frame generation which involves the flexible integration of three trainable adapters with frozen Latent Diffusion Models (LDMs) to focus on maintaining facial geometry and semantics as well as texture and temporal coherence between frames. In this way we inherit high-quality diverse generation from LDMs while significantly improving their controllability at a low training cost. Extensive experiments demonstrate the flexibility and effectiveness of our method in handling this paradigm. The codes will be released at https://github.com/modelscope/facechain.