Optimizing for aesthetically pleasing quadrotor camera motion

Abstract

Fig. 1. Quadrotor camera tools generate trajectories based on user-specified keyframes in time and space. Reasoning about spatio-temporal distances is hard for users and can lead to visually unappealing results and fluctuating camera velocities. Top row: user-specified keyframes (blue) are positioned in time, such that the camera first moves too slow and then needs to accelerate drastically to reach the specified end-point. Bottom row: results of our method which automatically positions keyframes (blue) in time such that the camera moves smoothly over the entire trajectory (illustrative example).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-theart 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.

Autonomous aerial cinematography in unstructured environments with learned artistic decision‐making

“…Several contributions to aerial cinematography focus on keyframe navigation. Gebhardt, Hepp, Nägeli, Stevšić, and Hilliges (, ), Joubert, Roberts, Truong, Berthouzoz, and Hanrahan (), Roberts and Hanrahan (), and Xie et al () provide user interface tools to retime and connect static aerial viewpoints to provide smooth and dynamically feasible trajectories, as well as a visually pleasing images. Lan, Shridhar, Hsu, and Zhao, () use key‐frames defined on the image itself instead of world coordinates.…”

Autonomous Drone Cinematographer: Using Artistic Principles to Create Smooth, Safe, Occlusion-Free Trajectories for Aerial Filming

“…Roberts and Hanrahan [28] generate off-line trajectories given potentially infeasible human-defined key-frames and Joubert et al [13] provide a tool for interactive offline design of camera trajectories. Similarly, Gebhardt et al [9] improve a user's time-parametrized trajectory, optimizing it for smooth motions. From user studies, Gebhardt et al [9] show that smoothness is key to producing visually-appealing videos.…”

Optimal Control for Electromagnetic Haptic Guidance Systems

“…To give the user freedom in deciding their drawing speed we first need to find the reference point s(θ) on the target trajectory s. Finding the closest point on the path is an optimization problem itself and hence can not be used within our optimization. Similar to recent work in robot trajectory generation [26,11], we decompose the distance to the closest point into a contouring and lag error, as shown in Figure 5. r θ is the vector between the pen p p and a point s(θ) on the spline, and n as the normalized tangent vector to the spline at that point, which is defined as n = ∂s(θ) ∂θ .…”