Autocomplete Repetitive Stroking with Image Guidance

TL;DR

This paper introduces a system that automatically predicts and autocompletes repetitive strokes in image-guided drawing, helping users create detailed textures efficiently while maintaining drawing fluidity.

Contribution

It presents a novel method for real-time stroke prediction during drawing by jointly analyzing image regions and user input history, enhancing workflow and pattern richness.

Findings

Reduces workload for repetitive stroke drawing

Enables seamless integration with user control

Improves pattern complexity in generated textures

Abstract

Image-guided drawing can compensate for the lack of skills but often requires a significant number of repetitive strokes to create textures. Existing automatic stroke synthesis methods are usually limited to predefined styles or require indirect manipulation that may break the spontaneous flow of drawing. We present a method to autocomplete repetitive short strokes during users' normal drawing process. Users can draw over a reference image as usual. At the same time, our system silently analyzes the input strokes and the reference to infer strokes that follow users' input style when certain repetition is detected. Users can accept, modify, or ignore the system predictions and continue drawing, thus maintaining the fluid control of drawing. Our key idea is to jointly analyze image regions and operation history for detecting and predicting repetitions. The proposed system can effectively reduce users' workload in drawing repetitive short strokes and facilitates users in creating results with rich patterns.