Autocomplete hand-drawn animations

Abstract

Hand-drawn animation is a major art form and communication medium, but can be challenging to produce. We present a system to help people create frame-by-frame animations through manual sketches. We design our interface to be minimalistic: it contains only a canvas and a few controls. When users draw on the canvas, our system silently analyzes all past sketches and predicts what might be drawn in the future across spatial locations and temporal frames. The interface also offers suggestions to beautify existing drawings. Our system can reduce manual workload and improve output quality without compromising natural drawing flow and control: users can accept, ignore, or modify such predictions visualized on the canvas by simple gestures. Our key idea is to extend the local similarity method in [Xing et al. 2014], which handles only low-level spatial repetitions such as hatches within a single frame, to a global similarity that can capture high-level structures across multiple frames such as dynamic objects. We evaluate our system through a preliminary user study and confirm that it can enhance both users' objective performance and subjective satisfaction.