WanPE: Towards Cinematic Prompt Enhancement for Modern Text-to-Video Generation
Video generation begins in text space by authoring a cinematic screenplay, then materializes into pixels. As contemporary video generators scale to 30 seconds and faithfully follow complex conditions, the textual prompt largely directs the production, planning how actions, camera trajectories, lighting, and sound unfold across multi-shot sequences. In this paper, we present Wan
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Video generation begins in text space by authoring a cinematic screenplay, then materializes into pixels. As contemporary video generators scale to 30 seconds and faithfully follow complex conditions, the textual prompt largely directs the production, planning how actions, camera trajectories, lighting, and sound unfold across multi-shot sequences. In this paper, we present Wan
This work introduces a large-scale prompt enhancement model specifically for cinematic text-to-video generation, addressing a critical bottleneck in creating high-quality, multi-shot video content. For ML engineers, understanding how such models translate high-level textual descriptions into detailed, shot-level cinematic plans is crucial for advancing video synthesis capabilities and developing more sophisticated creative AI tools.