Face Animation with an Attribute-Guided Diffusion Model

Understand

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.