SmartDirector: Keyframe-Conditioned Cinematic Video Generation with Narrative Pacing Control
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| Authors | Zhida Zhang et al. |
| Year | 2026 |
| HF Upvotes | 4 |
| arXiv | 2605.27891 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
The narrative quality of a video fundamentally determines its perceptual value. Although existing video generation methods can produce visually appealing content, they predominantly rely on sparse conditioning signals such as text prompts or first/last frames, which limits precise control over narrative structure and temporal pacing. In this paper, we propose SmartDirector, a framework that enhances the narrative capacity of video generation models through multiple keyframes. SmartDirector supports flexible generation scenarios including single-shot generation, multi-shot narrative synthesis, and video extension. The framework operates in two stages: Director-Gen generates a low-resolution video conditioned on the provided keyframes, and Director-SR refines the output by exploiting high-resolution keyframes as semantic anchors to recover fine-grained details. To enable robust multi-keyframe training, we construct a data pipeline that curates single-shot and multi-shot sequences from movies. Extensive experiments demonstrate that SmartDirector substantially outperforms existing state-of-the-art approaches. We will release the code to facilitate further research.
Engineering Breakdown
The Problem
The narrative quality of a video fundamentally determines its perceptual value.
The Approach
In this paper, we propose SmartDirector, a framework that enhances the narrative capacity of video generation models through multiple keyframes.
Key Results
Extensive experiments demonstrate that SmartDirector substantially outperforms existing state-of-the-art approaches.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Smartdirector
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