Title: Autocomplete 3D Sculpting,
Abstract: Digital sculpting is a popular means to create 3D models but remains a
challenging task for many users. This can be alleviated by recent advances in
data-driven and procedural modeling, albeit bounded by the underlying data and
procedures. We propose a 3D sculpting system that assists users in freely
creating models without predefined scope. With a brushing interface similar to
common sculpting tools, our system silently records and analyzes users'
workflows, and predicts what they might or should do in the future to reduce
input labor or enhance output quality. Users can accept, ignore, or modify the
suggestions and thus maintain full control and individual style. They can also
explicitly select and clone past workflows over output model regions. Our key
idea is to consider how a model is authored via dynamic workflows in addition
to what it is shaped in static geometry, for more accurate analysis of user
intentions and more general synthesis of shape structures. The workflows
contain potential repetitions for analysis and synthesis, including user inputs
(e.g. pen strokes on a pressure sensing tablet), model outputs (e.g. extrusions
on an object surface), and camera viewpoints. We evaluate our method via user
feedbacks and authored models.