Jingwan (Cynthia) Lu will present her preFPO on Monday December 9 at 4:30 PM in Room 402. The members of her committee are: Adam Finkelstein, advisor; Szymon Rusinkiewicz and Stephen DiVerdi (Google), readers; Tom Funkhouser and Jianxiong Xiao, nonreaders. Everyone is invited to attend her talk. Her abstract follows below. ----- Original Message ----- Title: Data-driven Digital Drawing and Painting Abstract: Digital artists create evocative drawings and paintings using a tablet and stylus coupled with painting software like Adobe Photoshop or Corel Painter. Several research systems have shown promising improvements in various aspects of the art creation process by targeting specific drawing styles and natural media. Such systems combine carefully hand-crafted procedural rules and computationally expensive, style-specific physical simulations. Nevertheless, untrained users often find it hard to achieve their target style in these systems due to the challenge of controlling and predicting the outcome of their collective strokes. Moreover even trained digital artists are often restricted by the inherent stylistic limitations of these systems. In this thesis, I propose an alternative painting paradigm that allows novices to create artworks closely following an example style of their choice. The style is characterized by a set of example brush strokes. We present three prototype painting systems, all of which follow a common sketching interface, wherein the user's 1D paths are automatically replaced by complex 2D strokes shaped and textured by a small set of exemplars. The three prototype systems HelpingHand, RealBrush and DecoBrush make use of different types of exemplars for distinct styles of artwork. HelpingHand learns from hand gesture data to hallucinate the possible gestures used during drawing, which then drive a chosen virtual brush simulation engine to produce stylistic handwritings or line drawings. RealBrush uses photographed natural media stroke marks to stylize the user's query path, completely obviating the need for physical simulation. It also stylizes the stroke interactions based on captured physical examples. DecoBrush further generalizes the conventional "painting" concept beyond the scope of natural media to allow "painting" structured decorative vector patterns following user-sketched path, which alleviates the difficulty of creating such patterns from scratch in programs like Adobe Illustrator. All three systems address the common challenge of predicting the appearance of user's new query paths being faithful to the style of a small number of chosen exemplars. With the convenience of data sharing on the internet, this data-driven paradigm opens up new opportunities for artists and novices to create original stylistic artworks and to abstract, share, and reproduce their styles more easily and faithfully.