DiffusionCinema: Text-to-Aerial Cinematography
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911396410687488 |
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| author | Serpiva, Valerii Lykov, Artem Sam, Jeffrin Fedoseev, Aleksey Tsetserukou, Dzmitry |
| author_facet | Serpiva, Valerii Lykov, Artem Sam, Jeffrin Fedoseev, Aleksey Tsetserukou, Dzmitry |
| contents | We propose a novel Unmanned Aerial Vehicles (UAV) assisted creative capture system that leverages diffusion models to interpret high-level natural language prompts and automatically generate optimal flight trajectories for cinematic video recording. Instead of manually piloting the drone, the user simply describes the desired shot (e.g., "orbit around me slowly from the right and reveal the background waterfall"). Our system encodes the prompt along with an initial visual snapshot from the onboard camera, and a diffusion model samples plausible spatio-temporal motion plans that satisfy both the scene geometry and shot semantics. The generated flight trajectory is then executed autonomously by the UAV to record smooth, repeatable video clips that match the prompt. User evaluation using NASA-TLX showed a significantly lower overall workload with our interface (M = 21.6) compared to a traditional remote controller (M = 58.1), demonstrating a substantial reduction in perceived effort. Mental demand (M = 11.5 vs. 60.5) and frustration (M = 14.0 vs. 54.5) were also markedly lower for our system, confirming clear usability advantages in autonomous text-driven flight control. This project demonstrates a new interaction paradigm: text-to-cinema flight, where diffusion models act as the "creative operator" converting story intentions directly into aerial motion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17412 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DiffusionCinema: Text-to-Aerial Cinematography Serpiva, Valerii Lykov, Artem Sam, Jeffrin Fedoseev, Aleksey Tsetserukou, Dzmitry Robotics We propose a novel Unmanned Aerial Vehicles (UAV) assisted creative capture system that leverages diffusion models to interpret high-level natural language prompts and automatically generate optimal flight trajectories for cinematic video recording. Instead of manually piloting the drone, the user simply describes the desired shot (e.g., "orbit around me slowly from the right and reveal the background waterfall"). Our system encodes the prompt along with an initial visual snapshot from the onboard camera, and a diffusion model samples plausible spatio-temporal motion plans that satisfy both the scene geometry and shot semantics. The generated flight trajectory is then executed autonomously by the UAV to record smooth, repeatable video clips that match the prompt. User evaluation using NASA-TLX showed a significantly lower overall workload with our interface (M = 21.6) compared to a traditional remote controller (M = 58.1), demonstrating a substantial reduction in perceived effort. Mental demand (M = 11.5 vs. 60.5) and frustration (M = 14.0 vs. 54.5) were also markedly lower for our system, confirming clear usability advantages in autonomous text-driven flight control. This project demonstrates a new interaction paradigm: text-to-cinema flight, where diffusion models act as the "creative operator" converting story intentions directly into aerial motion. |
| title | DiffusionCinema: Text-to-Aerial Cinematography |
| topic | Robotics |
| url | https://arxiv.org/abs/2601.17412 |