Audio-visual Controlled Video Diffusion with Masked Selective State Spaces Modeling for Natural Talking Head Generation
This work addresses the practical utility of talking head generation for virtual avatars and human-computer interaction by enabling flexible multi-modal control, though it appears incremental as it builds on existing diffusion and mamba methods.
The paper tackled the problem of talking head synthesis being limited to single-modality control by introducing ACTalker, a video diffusion framework that supports multi-signal control, resulting in natural-looking facial videos with seamless integration of multiple driving modalities without conflict.
Talking head synthesis is vital for virtual avatars and human-computer interaction. However, most existing methods are typically limited to accepting control from a single primary modality, restricting their practical utility. To this end, we introduce \textbf{ACTalker}, an end-to-end video diffusion framework that supports both multi-signals control and single-signal control for talking head video generation. For multiple control, we design a parallel mamba structure with multiple branches, each utilizing a separate driving signal to control specific facial regions. A gate mechanism is applied across all branches, providing flexible control over video generation. To ensure natural coordination of the controlled video both temporally and spatially, we employ the mamba structure, which enables driving signals to manipulate feature tokens across both dimensions in each branch. Additionally, we introduce a mask-drop strategy that allows each driving signal to independently control its corresponding facial region within the mamba structure, preventing control conflicts. Experimental results demonstrate that our method produces natural-looking facial videos driven by diverse signals and that the mamba layer seamlessly integrates multiple driving modalities without conflict. The project website can be found at https://harlanhong.github.io/publications/actalker/index.html.