SculptStat: Statistical Analysis of Digital Sculpting Workflows

TL;DR

This paper analyzes digital sculpting workflows by recording and statistically analyzing artist behavior, revealing insights into stroke length, focus regions, and temporal editing patterns, to better understand expert modeling practices.

Contribution

It introduces a comprehensive statistical analysis method for digital sculpting workflows, capturing spatial, temporal, and distributional properties of artist interactions.

Findings

Artists mainly use short strokes influenced by model features.

Work is characterized by bursts rather than steady progress.

Artists revisit regions multiple times without periodic patterns.

Abstract

Targeted user studies are often employed to measure how well artists can perform specific tasks. But these studies cannot properly describe editing workflows as wholes, since they guide the artists both by choosing the tasks and by using simplified interfaces. In this paper, we investigate digital sculpting workflows used to produce detailed models. In our experiment design, artists can choose freely what and how to model. We recover whole-workflow trends with sophisticated statistical analyzes and validate these trends with goodness-of-fits measures. We record brush strokes and mesh snapshots by instrumenting a sculpting program and analyze the distribution of these properties and their spatial and temporal characteristics. We hired expert artists that can produce relatively sophisticated models in short time, since their workflows are representative of best practices. We analyze 13 meshes corresponding to roughly 25 thousand strokes in total. We found that artists work mainly with short strokes, with average stroke length dependent on model features rather than the artist itself. Temporally, artists do not work coarse-to-fine but rather in bursts. Spatially, artists focus on some selected regions by dedicating different amounts of edits and by applying different techniques. Spatio-temporally, artists return to work on the same area multiple times without any apparent periodicity. We release the entire dataset and all code used for the analyzes as reference for the community.