cs.AIApr 10, 2026
SaveCamera Artist: A Multi-Agent Framework for Cinematic Language Storytelling Video Generation
Organizations: School of Information and Communication Engineering, Communication University of China, Beijing, China · State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China
Abstract
We propose Camera Artist, a multi-agent framework that models a real-world filmmaking workflow to generate narrative videos with explicit cinematic language. While recent multi-agent systems have made substantial progress in automating filmmaking workflows from scripts to videos, they often lack explicit mechanisms to structure narrative progression across adjacent shots and deliberate use of cinematic language, resulting in fragmented storytelling and limited filmic quality. To address this, Camera Artist builds upon established agentic pipelines and introduces a dedicated Cinematography Shot Agent, which integrates recursive storyboard generation to strengthen shot-to-shot narrative continuity and cinematic language injection to produce more expressive, film-oriented shot designs. Extensive quantitative and qualitative results demonstrate that our approach consistently outperforms existing baselines in narrative consistency, dynamic expressiveness, and perceived film quality.
Figures & tables
Fig. 1: Comparison with multi-agent system on filmic storytelling. Existing multi-agent methods tend to exhibit fragmented narratives and weak cinematic control. In contrast, Camera Artist achieves stronger shot-to-shot coherence and richer cinematic expression, yielding more filmic storytelling.
Fig. 2: The overall framework of Camera Artist. Camera Artist operates in two stages: footage construction and shot generation . In the footage construction stage, the Director Agent expands the story outline and builds hierarchical storyboard assets at script, scene, and shot levels. In the shot generation stage, the Cinematography Shot Agent first performs recursive shot generation to ensure narrative coherence, and then injects cinematic language to refine shot descriptions. Finally, the Video Generation Agent produces shot-wise videos and stitches them into a complete long-form narrative film.
Fig. 3: Mechanism of the Cinematography Shot Agent. (a) Recursive Shots Generation (RSG): By recursively generating shots and selecting start/mid/end types, the system produces storyboards with strong narrative coherence. (b) Cinematic Language Injection (CLI): A fine-tuned LLM trained on professional cinematic language transforms original shot descriptions into film-style, cinematically expressive ones.
Fig. 4: Qualitative experimental results of single shot content. For videos with similar shot content, Camera Artist can achieve richer and more expressive cinematic language, outperforming prior multi-agent methods.
| Semantic Consistency | VBench Metrics | |||||
|---|---|---|---|---|---|---|
| Method/Metrics | CLIP-T ( ) | Subj.( ) | Bg.( ) | Motion( ) | Dyn.( ) | Aesth.( ) |
| VGoT [ 21 ] | 28.15 | 78.58 | 97.93 | 99.27 | 16.67 | 68.73 |
| Anim-Director [ 11 ] | 23.86 | 67.79 | 94.15 | 96.54 | 39.78 | 67.24 |
| MovieAgent [ 18 ] | 22.25 | 71.01 | 94.52 | 98.00 | 76.27 | 65.63 |
| Ours | 29.61 | 79.54 | 96.26 | 99.32 | 80.00 | 69.51 |
TABLE I: Quantitative comparison using VBench and CLIP-based semantic consistency. Best and second-best results are highlighted in blue and green , respectively.
| Script Cons. ( ) | Cam. Cons. ( ) | Video Qual. ( ) | Real. Sim. ( ) | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Method/Metrics | GPT-4o | Qwen3 | Gemini | Avg. | GPT-4o | Qwen3 | Gemini | Avg. | GPT-4o | Qwen3 | Gemini | Avg. | GPT-4o | Qwen3 | Gemini | Avg. |
| VGoT [ 21 ] | 3.33 | 3.00 | 2.17 | 2.83 | 1.83 | 1.00 | 1.17 | 1.33 | 4.67 | 4.83 | 2.67 | 4.06 | 4.17 | 3.67 | 2.67 | 3.50 |
| Anim-Director [ 11 ] | 3.60 | 2.50 | 2.83 | 2.98 | 2.40 | 1.00 | 3.50 | 2.30 | 3.33 | 3.17 | 2.67 | 3.06 | 2.00 | 1.83 | 1.00 | 1.61 |
| MovieAgent [ 18 ] | 2.10 | 1.30 | 3.17 | 2.19 | 1.70 | 1.00 | 3.38 | 2.03 | 4.10 | 4.30 | 4.00 | 4.13 | 2.90 | 2.70 | 3.80 | 3.13 |
| Ours | 4.50 | 4.00 | 3.20 | 3.90 | 3.25 | 3.90 | 3.50 | 3.55 | 4.86 | 4.78 | 4.50 | 4.71 | 4.00 | 4.56 | 3.50 | 4.02 |
TABLE II: Multi-VLM evaluation across narrative and cinematic dimensions. Best and second-best results are highlighted in blue and green , respectively.
| Semantic | VBench Metrics | LLM-based Evaluation | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Method | CLIP-T | Subj.( ) | Bg.( ) | Motion( ) | Dyn.( ) | Aesth.( ) | Script Cons.( ) | Cam. Cons.( ) | Video Qual.( ) | Real. Sim.( ) |
| w/o RSG | 28.22 | 74.69 | 93.93 | 99.04 | 78.67 | 67.45 | 3.55 | 3.36 | 4.45 | 3.91 |
| w/o CLI | 29.27 | 73.49 | 94.33 | 98.97 | 74.25 | 67.10 | 3.60 | 2.83 | 4.00 | 3.67 |
| Camera Artist | 29.61 | 79.54 | 96.26 | 99.32 | 80.00 | 69.51 | 3.90 | 3.55 | 4.71 | 4.02 |
TABLE III: Quantitative results of the ablation study on recursive storyboard generation and cinematic language injection. Best and second-best results are highlighted in blue and green , respectively.
Fig. 5: Qualitative comparison of inter-shot narrative coherence. Camera Artist conditions each shot on preceding shot and scene information, producing shot content that is narratively coherent in both text and visual realization.
Fig. 6: User study comparison on four subjective metrics. Results of VGoT [ 21 ] , Anim-Director [ 11 ] , MovieAgent [ 18 ] , and our method on Script Consistency, Camera-Movement Consistency, Video Quality, and Real-Movie Similarity. Our method achieves the highest scores across all metrics.
Fig. 7: Ablation study on RSG and CLI. RSG preserves coherent shot-to-shot narrative flow, while CLI enhances cinematic expressiveness through deliberate camera motion and lighting; removing either results in fragmented storytelling or visually static shots.
Appendix figures & tables7 assets
Supplementary material from the paper’s appendix.
Appendix
Fig. 8: Camera Artist workflow visualization. Given a user-provided story outline, Camera Artist decomposes the narrative into structured scene plots and character assets via the Director Agent , refines them into coherent shot-level descriptions with explicit cinematic language using the Cinematography Shot Agent , and finally renders corresponding visual clips through the Video Generation Agent . The collaboration among agents enables automated long-form video generation with coherent narrative progression and expressive cinematic shot design.
Fig. 9: An example of pipeline for cinematic language LoRA fine-tuning. Ordinary captions are produced by a VLM from raw video, while ShotBench [ 12 ] provides shot-level cinematic annotations. A LoRA-tuned LLM learns to transform ordinary captions into cinematic shot descriptions with explicit cinematic language, which are later used for cinematic language injection during inference.
Fig. 10: The CoT Description of Camera Artist. (a) The CoT of Director Agent, which is mainly responsible for the expansion of script content and scene splitting.(b) The CoT of Cinematography Shot Agent, which is mainly responsible for the recursive generation of storyboard content and the introduction of shot language.
Fig. 11: The CoT prompting of VLM-based evaluation. The CoT is mainly responsible for the recursive generation of storyboard content and the introduction of shot language.
Fig. 12: Qualitative comparison with baseline methoda. (a) Camera Artist generates a final wide shot with high-angle composition and slow pull-back movement, delivering stronger cinematic atmosphere and expressive visual storytelling. (b) Baselines introduce irrelevant characters or exhibit abrupt narrative jumps in two-shot sequences, while Camera Artist maintains both character/scene consistency and coherent event progression.
Fig. 13: Reference-free storytelling video generation. Given only a textual story outline (no character reference images), Camera Artist automatically constructs scenes, characters, and shot sequences, producing a long-form narrative video with coherent story progression and cinematic visual expression.
Fig. 14: Additional qualitative results. Scene-level keyframes together with the corresponding footage are presented, illustrating coherent long-range storytelling, consistent character depiction, and film-style visual expression.