# Optimizing for Aesthetically Pleasing Quadrotor Camera Motion

arXiv: https://arxiv.org/abs/1906.11686 · HTML: https://mlsanity.com/paper/1906.11686/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning](https://mlsanity.com/paper/2109.09923/) (2021)
- [3D Human Reconstruction in the Wild with Collaborative Aerial Cameras](https://mlsanity.com/paper/2108.03936/) (2021)
- [LensCraft: Your Professional Virtual Cinematographer](https://mlsanity.com/paper/2506.00988/) (2025)
- [Active Human Pose Estimation via an Autonomous UAV Agent](https://mlsanity.com/paper/2407.01811/) (2024)

## Abstract (arXiv)

In this paper we first contribute a large scale online study (N=400) to better understand aesthetic perception of aerial video. The results indicate that it is paramount to optimize smoothness of trajectories across all keyframes. However, for experts timing control remains an essential tool. Satisfying this dual goal is technically challenging because it requires giving up desirable properties in the optimization formulation. Second, informed by this study we propose a method that optimizes positional and temporal reference fit jointly. This allows to generate globally smooth trajectories, while retaining user control over reference timings. The formulation is posed as a variable, infinite horizon, contour-following algorithm. Finally, a comparative lab study indicates that our optimization scheme outperforms the state-of-the-art in terms of perceived usability and preference of resulting videos. For novices our method produces smoother and better looking results and also experts benefit from generated timings.