DramaDirector: Geometry-Guided Short Drama Generation

2026-06-22

AI Summary

Existing text- or prompt-driven video generation methods struggle to meet the demands of short-form dramas, which require rapid shot transitions, dialogue-driven focus shifts, and cinematic visual composition. To address this, we propose a geometry-guided framework for short drama generation that decouples static visual structure from dynamic narrative conditions, leveraging depth and pose priors to guide initial frame synthesis and image-to-video generation. We introduce DramaBoard, the first structured storyboard dataset for short dramas, and design a constrained training mechanism incorporating text–visual alignment rewards, schema-constrained supervised fine-tuning, and GRPO-based reinforcement learning. Experiments demonstrate that our approach significantly outperforms existing baselines in terms of fidelity, temporal consistency, and controllability. We publicly release our code and the DramaBoard evaluation benchmark.

Abstract

Short dramas, with their rapid shot rhythms, dialogue-driven focus shifts, and demanding cinematographic grounding, pose challenges that prompt-level or text-only video generation pipelines struggle to meet. We study plot-to-short-drama generation, where a global plot and local context are transformed into visually grounded multi-shot videos. We propose DramaDirector, a geometry-grounded framework that lets the planner borrow cinematographic geometry from a gallery of real short-drama shots indexed by depth and pose. DramaDirector decouples each shot into static visual and dynamic narrative conditions, trains the planner with schema-constrained SFT and GRPO under a learned text-visual alignment reward, and retrieves depth-pose references to guide first-frame generation and image-to-video synthesis. We also introduce DramaBoard, a benchmark built from 35 live-action dramas, 2.8K episodes, and 81K shots, with structured storyboards and multi-dimensional evaluation protocols. Experiments show that DramaDirector improves over representative multi-agent and video generation baselines on faithfulness, consistency, and controllability. Our code is released at: https://github.com/iLearn-Lab/DramaDirector

Problem

• short drama generation
• cinematographic grounding
• multi-shot video
• visual consistency
• plot-to-video

Innovation

• geometry-guided generation
• short drama synthesis
• depth-pose retrieval
• text-visual alignment
• structured storyboard