FantasyTalking: Realistic Talking Portrait Generation via Coherent
  Motion Synthesis
Authors: name: Mengchao Wang, name: Qiang Wang, name: Fan Jiang, name: Yaqi Fan, name: Yunpeng Zhang, name: Yonggang Qi, name: Kun Zhao, name: Mu Xu

Creating a realistic animatable avatar from a single static portrait remains
challenging. Existing approaches often struggle to capture subtle facial
expressions, the associated global body movements, and the dynamic background.
To address these limitations, we propose a novel framework that leverages a
pretrained video diffusion transformer model to generate high-fidelity,
coherent talking portraits with controllable motion dynamics. At the core of
our work is a dual-stage audio-visual alignment strategy. In the first stage,
we employ a clip-level training scheme to establish coherent global motion by
aligning audio-driven dynamics across the entire scene, including the reference
portrait, contextual objects, and background. In the second stage, we refine
lip movements at the frame level using a lip-tracing mask, ensuring precise
synchronization with audio signals. To preserve identity without compromising
motion flexibility, we replace the commonly used reference network with a
facial-focused cross-attention module that effectively maintains facial
consistency throughout the video. Furthermore, we integrate a motion intensity
modulation module that explicitly controls expression and body motion
intensity, enabling controllable manipulation of portrait movements beyond mere
lip motion. Extensive experimental results show that our proposed approach
achieves higher quality with better realism, coherence, motion intensity, and
identity preservation. Ours project page:
https://fantasy-amap.github.io/fantasy-talking/.