  Face animation has achieved much progress in computer vision. However,
prevailing GAN-based methods suffer from unnatural distortions and artifacts
due to sophisticated motion deformation. In this paper, we propose a Face
Animation framework with an attribute-guided Diffusion Model (FADM), which is
the first work to exploit the superior modeling capacity of diffusion models
for photo-realistic talking-head generation. To mitigate the uncontrollable
synthesis effect of the diffusion model, we design an Attribute-Guided
Conditioning Network (AGCN) to adaptively combine the coarse animation features
and 3D face reconstruction results, which can incorporate appearance and motion
conditions into the diffusion process. These specific designs help FADM rectify
unnatural artifacts and distortions, and also enrich high-fidelity facial
details through iterative diffusion refinements with accurate animation
attributes. FADM can flexibly and effectively improve existing animation
videos. Extensive experiments on widely used talking-head benchmarks validate
the effectiveness of FADM over prior arts.
