MoDiTalker: Motion-Disentangled Diffusion Model for High-Fidelity
  Talking Head Generation
Seyeon Kim Siyoon Jin Jihye Park Kihong Kim Jiyoung Kim Jisu Nam Seungryong Kim
Conventional GAN-based models for talking head generation often suffer from
limited quality and unstable training. Recent approaches based on diffusion
models aimed to address these limitations and improve fidelity. However, they
still face challenges, including extensive sampling times and difficulties in
maintaining temporal consistency due to the high stochasticity of diffusion
models. To overcome these challenges, we propose a novel motion-disentangled
diffusion model for high-quality talking head generation, dubbed MoDiTalker. We
introduce the two modules: audio-to-motion (AToM), designed to generate a
synchronized lip motion from audio, and motion-to-video (MToV), designed to
produce high-quality head video following the generated motion. AToM excels in
capturing subtle lip movements by leveraging an audio attention mechanism. In
addition, MToV enhances temporal consistency by leveraging an efficient
tri-plane representation. Our experiments conducted on standard benchmarks
demonstrate that our model achieves superior performance compared to existing
models. We also provide comprehensive ablation studies and user study results.