# Batteries, camera, action! Learning a semantic control space for expressive robot cinematography

arXiv: https://arxiv.org/abs/2011.10118 · HTML: https://mlsanity.com/paper/2011.10118/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [LATTE: LAnguage Trajectory TransformEr](https://mlsanity.com/paper/2208.02918/) (2022)
- [Reshaping Robot Trajectories Using Natural Language Commands: A Study of Multi-Modal Data Alignment Using Transformers](https://mlsanity.com/paper/2203.13411/) (2022)
- [DanceCamera3D: 3D Camera Movement Synthesis with Music and Dance](https://mlsanity.com/paper/2403.13667/) (2024)
- [AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning](https://mlsanity.com/paper/2109.09923/) (2021)
- [LensCraft: Your Professional Virtual Cinematographer](https://mlsanity.com/paper/2506.00988/) (2025)
- [Automatic Camera Trajectory Control with Enhanced Immersion for Virtual Cinematography](https://mlsanity.com/paper/2303.17041/) (2023)

## Abstract (arXiv)

Aerial vehicles are revolutionizing the way film-makers can capture shots of actors by composing novel aerial and dynamic viewpoints. However, despite great advancements in autonomous flight technology, generating expressive camera behaviors is still a challenge and requires non-technical users to edit a large number of unintuitive control parameters. In this work, we develop a data-driven framework that enables editing of these complex camera positioning parameters in a semantic space (e.g. calm, enjoyable, establishing). First, we generate a database of video clips with a diverse range of shots in a photo-realistic simulator, and use hundreds of participants in a crowd-sourcing framework to obtain scores for a set of semantic descriptors for each clip. Next, we analyze correlations between descriptors and build a semantic control space based on cinematography guidelines and human perception studies. Finally, we learn a generative model that can map a set of desired semantic video descriptors into low-level camera trajectory parameters. We evaluate our system by demonstrating that our model successfully generates shots that are rated by participants as having the expected degrees of expression for each descriptor. We also show that our models generalize to different scenes in both simulation and real-world experiments. Data and video found at: https://sites.google.com/view/robotcam.