WanPE: Towards Cinematic Prompt Enhancement for Modern Text-to-Video Generation

arXiv:2609.30221 · cs.CV · Submitted 2026-09-24 · Read on arXiv

cs.CV

Submitted: 2026-09-24

Updated: 2026-09-24

Project page: https://wan-pe.github.io

Terminology

• GRPO — 34× in this paper · explained in 6 episodes
• Consistency — 22× in this paper · explained in 3 episodes
• Fidelity — 7× in this paper · explained in 2 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Hallucination — 2× in this paper · explained in 4 episodes
• International Conference on Computer Vision — 2× in this paper
• Transferability — 2× in this paper · explained in 4 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• PhyPrompt: RL-based Prompt Refinement for Physically Plausible Text-to-Video Generation
• CAPE-T2V: Captioner-Anchored Prompt Enhancement toward Two-Sided Conditioning Alignment in Text-to-Video Generation
• POS: A Prompts Optimization Suite for Augmenting Text-to-Video Generation
• RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via Data Alignment and Test-Time Scaling
• Wan: Open and Advanced Large-Scale Video Generative Models
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Seedance 2.0: Advancing Video Generation for World Complexity
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
• AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
• Latte: Latent Diffusion Transformer for Video Generation
• Open-Sora: Democratizing Efficient Video Production for All
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
• HunyuanVideo 1.5 Technical Report
• Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence

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