Audio-visual Controlled Video Diffusion with Masked Selective State
  Spaces Modeling for Natural Talking Head Generation
Authors: name: Fa-Ting Hong, name: Zunnan Xu, name: Zixiang Zhou, name: Jun Zhou, name: Xiu Li, name: Qin Lin, name: Qinglin Lu, name: Dan Xu

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 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.