Wan-S2V: Audio-Driven Cinematic Video Generation
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.
68ae77be364411bea07df771
Xin Gao
68ae77be364411bea07df772
Li Hu
68ae77be364411bea07df773
Siqi Hu
68ae77be364411bea07df774
Mingyang Huang
68ae77be364411bea07df775
Chaonan Ji
68ae77be364411bea07df776
Dechao Meng
68ae77be364411bea07df777
Jinwei Qi
68ae77be364411bea07df778
Penchong Qiao
68ae77be364411bea07df779
Zhen Shen
68ae77be364411bea07df77a
Yafei Song
68ae77be364411bea07df77b
Ke Sun
68ae77be364411bea07df77c
Linrui Tian
68ae77be364411bea07df77d
Guangyuan Wang
68ae77be364411bea07df77e
Qi Wang
68ae77be364411bea07df77f
Zhongjian Wang
68ae77be364411bea07df780
Jiayu Xiao
68ae77be364411bea07df781
Sheng Xu
68ae77be364411bea07df782
Bang Zhang
68ae77be364411bea07df783
Peng Zhang
68ae77be364411bea07df784
Xindi Zhang
68ae77be364411bea07df785
Zhe Zhang
68ae77be364411bea07df786
Jingren Zhou
68ae77be364411bea07df787
Lian Zhuo
Wan-S2V: Audio-Driven Cinematic Video Generation
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.
Wan-S2V, an audio-driven model built on Wan, enhances expressiveness and fidelity in cinematic character animation compared to existing methods.
audio-driven model Wan-S2V Wan Hunyuan-Avatar Omnihuman long-form video generation video lip-sync editing