Rubab, FizzaTong, YiyingKnoll, AaronPeters, Christoph2025-06-202025-06-202025978-3-03868-291-22079-8687https://doi.org/10.2312/hpg.20251169https://diglib.eg.org/handle/10.2312/hpg20251169Recent advances in implicit neural representations have made them a popular choice for modeling 3D geometry. However, directly editing these representations presents challenges due to the complex relationship between model weights and surface geometry, as well as the slow optimization required to update neural fields. Among various editing tools, sculpting stands out as a valuable operation for the graphics and modeling community. While traditional mesh-based tools like ZBrush enable intuitive edits, a comparable high-performance toolkit for sculpting neural SDFs is currently lacking. We introduce a framework that enables interactive surface sculpting directly on neural implicit representations with optimized performance. Unlike previous methods, which are limited to spot edits, our approach allows users to perform stroke-based modifications on the fly, ensuring intuitive shape manipulation without switching representations. By employing tubular neighborhoods to sample strokes and customizable brush profiles, we achieve smooth deformations along user-defined curves, providing intuitive control over the sculpting process. Our method demonstrates that versatile edits can be achieved while preserving the smooth nature of implicit representations, all without compromising interactive performance.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Computer graphics; Machine learningComputing methodologies → Computer graphicsMachine learning10.2312/hpg.2025116910 pages