# AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning

arXiv: https://arxiv.org/abs/2109.09923 · HTML: https://mlsanity.com/paper/2109.09923/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Making Convolutional Networks Shift-Invariant Again](https://mlsanity.com/paper/1904.11486/) (2019)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Image Aesthetic Assessment: An Experimental Survey](https://mlsanity.com/paper/1610.00838/) (2016)
- [Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study](https://mlsanity.com/paper/1701.01480/) (2017)
- [Learning to Compose with Professional Photographs on the Web](https://mlsanity.com/paper/1702.00503/) (2017)
- [Autonomous Aerial Cinematography In Unstructured Environments With Learned Artistic Decision-Making](https://mlsanity.com/paper/1910.06988/) (2019)
- [Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematography](https://mlsanity.com/paper/1904.02579/) (2019)
- [Batteries, camera, action! Learning a semantic control space for expressive robot cinematography](https://mlsanity.com/paper/2011.10118/) (2020)
- [Creatism: A deep-learning photographer capable of creating professional work](https://mlsanity.com/paper/1707.03491/) (2017)
- [Optimizing for Aesthetically Pleasing Quadrotor Camera Motion](https://mlsanity.com/paper/1906.11686/) (2019)
- [Towards a Drone Cinematographer: Guiding Quadrotor Cameras using Visual Composition Principles](https://mlsanity.com/paper/1610.01691/) (2016)
- [Airways: Optimization-Based Planning of Quadrotor Trajectories according to High-Level User Goals](https://mlsanity.com/paper/1906.11669/) (2019)

## Built on by (most cited first, 7 of 7)

- [PhotoFramer: Multi-modal Image Composition Instruction](https://mlsanity.com/paper/2512.00993/) (2025)
- [A Reinforcement Learning-Based Automatic Video Editing Method Using Pre-trained Vision-Language Model](https://mlsanity.com/paper/2411.04942/) (2024)
- [PhotoBot: Reference-Guided Interactive Photography via Natural Language](https://mlsanity.com/paper/2401.11061/) (2024)
- [3D View Optimization for Improving Image Aesthetics](https://mlsanity.com/paper/2405.16443/) (2024)
- [Aesthetic Camera Viewpoint Suggestion with 3D Aesthetic Field](https://mlsanity.com/paper/2602.20363/) (2026)
- [PhotoAgent: A Robotic Photographer with Spatial and Aesthetic Understanding](https://mlsanity.com/paper/2603.22796/) (2026)
- [PhotoFlow: Agentic 3D Virtual Photography Missions](https://mlsanity.com/paper/2605.23771/) (2026)

## Abstract (arXiv)

The process of capturing a well-composed photo is difficult and it takes years of experience to master. We propose a novel pipeline for an autonomous agent to automatically capture an aesthetic photograph by navigating within a local region in a scene. Instead of classical optimization over heuristics such as the rule-of-thirds, we adopt a data-driven aesthetics estimator to assess photo quality. A reinforcement learning framework is used to optimize the model with respect to the learned aesthetics metric. We train our model in simulation with indoor scenes, and we demonstrate that our system can capture aesthetic photos in both simulation and real world environments on a ground robot. To our knowledge, this is the first system that can automatically explore an environment to capture an aesthetic photo with respect to a learned aesthetic estimator.