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Title: AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning
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. Source code is at https://github.com/HadiZayer/AutoPhoto  more » « less
Award ID(s):
1900783
NSF-PAR ID:
10377841
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Page Range / eLocation ID:
944 to 951
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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