AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning
This addresses the challenge of aesthetic photo capture for users without photography expertise, representing a novel application rather than an incremental improvement.
The paper tackles the problem of capturing well-composed photos by proposing an autonomous agent that uses reinforcement learning to navigate and optimize photo quality based on a learned aesthetics metric, demonstrating successful photo capture in both simulation and real-world environments on a ground robot.
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.