Wan-S2V：音频驱动的电影级视频生成
Wan-S2V: Audio-Driven Cinematic Video Generation
AI总结：
针对音频驱动角色动画在复杂影视场景中的不足，提出基于Wan的Wan-S2V模型，显著提升表现力与保真度，在基准测试中优于现有方法，并支持长视频生成和唇形同步编辑。
摘要：
当前最先进的（SOTA）音频驱动角色动画方法在主要涉及语音和歌唱的场景中表现出良好的性能。然而，它们在更复杂的影视制作中往往表现不足，这些制作需要精细的角色互动、逼真的身体动作和动态的镜头运动等复杂元素。为了解决这一实现电影级角色动画的长期挑战，我们提出了一种基于Wan构建的音频驱动模型，称为Wan-S2V。与现有方法相比，我们的模型在电影情境中显著增强了表现力和保真度。我们进行了大量实验，将我们的方法与Hunyuan-Avatar和Omnihuman等前沿模型进行了基准测试。实验结果一致表明，我们的方法显著优于这些现有解决方案。此外，我们还通过其在长视频生成和精确视频唇形同步编辑中的应用探索了该方法的多样性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
