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The final outcome is shown in (c). Our system works for both individual strokes (e.g. the detailed hatches) and structured elements (e.g. the windows). Due to the spatial and temporal variations, these sketches are very difficult, if not impossible, to be generated via prior sequential clone systems. AbstractPainting is a major form of content creation, offering unlimited control and freedom of expression. However, it can involve tedious manual repetitions, such as stippling large regions or hatching complex contours. Thus, a central goal in digital painting research is to automate tedious repetitions while allowing user control. Existing methods impose a sequential order, in which a small exemplar is prepared and then cloned through additional gestures. Such sequential mode may break the continuous, spontaneous flow of painting. Moreover, it is more suitable for homogeneous areas than nuanced variations common in real paintings.We present an interactive digital painting system that autocompletes tedious repetitions while preserving nuanced variations and maintaining natural flows. Specifically, users paint as usual, while our system records and analyzes their workflows. When potential repetition is detected, our system predicts what the user might want to draw and offers auto-completes that adjust to the existing shape-color context. Our method eliminates the need for sequential creation-cloning and better adapts to the local painting contexts. Furthermore, users can choose to accept, ignore, or modify those predictions and thus maintain full control. Our method can be considered as the painting analogy of auto-completes in common typing and IDE systems. We demonstrate the quality and usability of our system through painting results and a pilot user study.

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Revision control is a vital component of digital project management and has been widely deployed for text files. Binary files, on the other hand, have received relatively less attention. This can be inconvenient for graphics applications that use a significant amount of binary data, such as images, videos, meshes, and animations. Existing strategies such as storing whole files for individual revisions or simple binary deltas could consume significant storage and obscure vital semantic information. We present a nonlinear revision control system for images, designed with the common digital editing and sketching workflows in mind. We use DAG (directed acyclic graph) as the core structure, with DAG nodes representing editing operations and DAG edges the corresponding spatial, temporal and semantic relationships. We visualize our DAG in RevG (revision graph), which provides not only as a meaningful display of the revision history but also an intuitive interface for common revision control operations such as review, replay, diff, addition, branching, merging, and conflict resolving. Beyond revision control, our system also facilitates artistic creation processes in common image editing and digital painting workflows. We have built a prototype system upon GIMP, an open source image editor, and demonstrate its effectiveness through formative user study and comparisons with alternative revision control systems.

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