# Agentic Aerial Cinematography: From Dialogue Cues to Cinematic Trajectories

arXiv: https://arxiv.org/abs/2509.16176 · HTML: https://mlsanity.com/paper/2509.16176/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 30 of its 40 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 30 of 30)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action](https://mlsanity.com/paper/2207.04429/) (2022)
- [Visual Language Maps for Robot Navigation](https://mlsanity.com/paper/2210.05714/) (2022)
- [Simple but Effective: CLIP Embeddings for Embodied AI](https://mlsanity.com/paper/2111.09888/) (2021)
- [AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One](https://mlsanity.com/paper/2312.06709/) (2023)
- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [Q-Frame: Query-aware Frame Selection and Multi-Resolution Adaptation for Video-LLMs](https://mlsanity.com/paper/2506.22139/) (2025)
- [Preferential Bayesian Optimization](https://mlsanity.com/paper/1704.03651/) (2017)
- [KeyVideoLLM: Towards Large-scale Video Keyframe Selection](https://mlsanity.com/paper/2407.03104/) (2024)
- [FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in Virtual 3D Spaces](https://mlsanity.com/paper/2501.12909/) (2025)
- [Revisit Anything: Visual Place Recognition via Image Segment Retrieval](https://mlsanity.com/paper/2409.18049/) (2024)
- [Autonomous Aerial Cinematography In Unstructured Environments With Learned Artistic Decision-Making](https://mlsanity.com/paper/1910.06988/) (2019)
- [Swarm-GPT: Combining Large Language Models with Safe Motion Planning for Robot Choreography Design](https://mlsanity.com/paper/2312.01059/) (2023)
- [ChatCam: Empowering Camera Control through Conversational AI](https://mlsanity.com/paper/2409.17331/) (2024)
- [Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematography](https://mlsanity.com/paper/1904.02579/) (2019)
- [Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing](https://mlsanity.com/paper/2309.06837/) (2023)
- [Which Viewpoint Shows it Best? Language for Weakly Supervising View Selection in Multi-view Instructional Videos](https://mlsanity.com/paper/2411.08753/) (2024)
- [CineMPC: A Fully Autonomous Drone Cinematography System Incorporating Zoom, Focus, Pose, and Scene Composition](https://mlsanity.com/paper/2401.05272/) (2024)
- [Test-Time Canonicalization by Foundation Models for Robust Perception](https://mlsanity.com/paper/2507.10375/) (2025)
- [PhotoBot: Reference-Guided Interactive Photography via Natural Language](https://mlsanity.com/paper/2401.11061/) (2024)
- [FM-Planner: Foundation Model Guided Path Planning for Autonomous Drone Navigation](https://mlsanity.com/paper/2505.20783/) (2025)
- [Automated Cinematography with Unmanned Aerial Vehicles](https://mlsanity.com/paper/1712.04353/) (2017)
- [Integrated Motion Planner for Real-time Aerial Videography with a Drone in a Dense Environment](https://mlsanity.com/paper/1911.09280/) (2019)
- [Efficient Visual Appearance Optimization by Learning from Prior Preferences](https://mlsanity.com/paper/2507.15355/) (2025)
- [Test-Time Augmentation for Pose-invariant Face Recognition](https://mlsanity.com/paper/2505.09256/) (2025)

## Built on by (most cited first, 2 of 2)

- [CinemaTraj: Composing Atomic Camera Trajectories for 3D Scenes with LLM Agents](https://mlsanity.com/paper/2607.26910/) (2026)
- [PhotoFlow: Agentic 3D Virtual Photography Missions](https://mlsanity.com/paper/2605.23771/) (2026)

## Abstract (arXiv)

We present Agentic Aerial Cinematography: From Dialogue Cues to Cinematic Trajectories (ACDC), an autonomous drone cinematography system driven by natural language communication between human directors and drones. The main limitation of previous drone cinematography workflows is that they require manual selection of waypoints and view angles based on predefined human intent, which is labor-intensive and yields inconsistent performance. In this paper, we propose employing large language models (LLMs) and vision foundation models (VFMs) to convert free-form natural language prompts directly into executable indoor UAV video tours. Specifically, our method comprises a vision-language retrieval pipeline for initial waypoint selection, a preference-based Bayesian optimization framework that refines poses using aesthetic feedback, and a motion planner that generates safe quadrotor trajectories. We validate ACDC through both simulation and hardware-in-the-loop experiments, demonstrating that it robustly produces professional-quality footage across diverse indoor scenes without requiring expertise in robotics or cinematography. These results highlight the potential of embodied AI agents to close the loop from open-vocabulary dialogue to real-world autonomous aerial cinematography.