Autocomplete 3D Sculpting

TL;DR

This paper introduces a user-assisted 3D sculpting system that predicts and suggests modeling actions based on dynamic workflows, enhancing creativity and efficiency in digital sculpting without limiting user freedom.

Contribution

It presents a novel workflow-based prediction system for 3D sculpting that analyzes user actions to assist and accelerate the modeling process.

Findings

System reduces user effort in 3D sculpting tasks.

User feedback indicates improved workflow efficiency.

Authors' models demonstrate flexible and accurate shape synthesis.

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.