Current state-of-the-art (SOTA) methods for audio-driven character animation
demonstrate promising performance for scenarios primarily involving speech and
singing. However, they often fall short in more complex film and television
productions, which demand sophisticated elements such as nuanced character
interactions, realistic body movements, and dynamic camera work. To address
this long-standing challenge of achieving film-level character animation, we
propose an audio-driven model, which we refere to as Wan-S2V, built upon Wan.
Our model achieves significantly enhanced expressiveness and fidelity in
cinematic contexts compared to existing approaches. We conducted extensive
experiments, benchmarking our method against cutting-edge models such as
Hunyuan-Avatar and Omnihuman. The experimental results consistently demonstrate
that our approach significantly outperforms these existing solutions.
Additionally, we explore the versatility of our method through its applications
in long-form video generation and precise video lip-sync editing.