# Autonomous Aerial Cinematography In Unstructured Environments With Learned Artistic Decision-Making

arXiv: https://arxiv.org/abs/1910.06988 · HTML: https://mlsanity.com/paper/1910.06988/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2019.

## Built on by (most cited first, 16 of 16)

- [E.T. the Exceptional Trajectories: Text-to-camera-trajectory generation with character awareness](https://mlsanity.com/paper/2407.01516/) (2024)
- [GenDoP: Auto-regressive Camera Trajectory Generation as a Director of Photography](https://mlsanity.com/paper/2504.07083/) (2025)
- [Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning](https://mlsanity.com/paper/2209.14624/) (2022)
- [JAWS: Just A Wild Shot for Cinematic Transfer in Neural Radiance Fields](https://mlsanity.com/paper/2303.15427/) (2023)
- [A Reinforcement Learning-Based Automatic Video Editing Method Using Pre-trained Vision-Language Model](https://mlsanity.com/paper/2411.04942/) (2024)
- [Pulp Motion: Framing-aware multimodal camera and human motion generation](https://mlsanity.com/paper/2510.05097/) (2025)
- [AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning](https://mlsanity.com/paper/2109.09923/) (2021)
- [ShotVerse: Advancing Cinematic Camera Control for Text-Driven Multi-Shot Video Creation](https://mlsanity.com/paper/2603.11421/) (2026)
- [3D Human Reconstruction in the Wild with Collaborative Aerial Cameras](https://mlsanity.com/paper/2108.03936/) (2021)
- [CLIPSwarm: Generating Drone Shows from Text Prompts with Vision-Language Models](https://mlsanity.com/paper/2403.13467/) (2024)
- [VERTIGO: Visual Preference Optimization for Cinematic Camera Trajectory Generation](https://mlsanity.com/paper/2604.02467/) (2026)
- [Greedy Perspectives: Multi-Drone View Planning for Collaborative Perception in Cluttered Environments](https://mlsanity.com/paper/2310.10863/) (2023)
- [Agentic Aerial Cinematography: From Dialogue Cues to Cinematic Trajectories](https://mlsanity.com/paper/2509.16176/) (2025)
- [Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV](https://mlsanity.com/paper/2203.02453/) (2022)
- [Automatic Camera Trajectory Control with Enhanced Immersion for Virtual Cinematography](https://mlsanity.com/paper/2303.17041/) (2023)
- [PhotoFlow: Agentic 3D Virtual Photography Missions](https://mlsanity.com/paper/2605.23771/) (2026)

## Abstract (arXiv)

Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is demand for an autonomous cinematographer that can reason about both geometry and scene context in real-time. Existing approaches do not address all aspects of this problem; they either require high-precision motion-capture systems or GPS tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow artistic guidelines specified before flight. In this work, we address the problem in its entirety and propose a complete system for real-time aerial cinematography that for the first time combines: (1) vision-based target estimation; (2) 3D signed-distance mapping for occlusion estimation; (3) efficient trajectory optimization for long time-horizon camera motion; and (4) learning-based artistic shot selection. We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in-depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at: https://youtu.be/ookhHnqmlaU.