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ACM MM2025Top-tier venue

Meta-Illustrator: Transferring Illustrations from 2D Interactive Image Space to 3D Immersive Exploration Space

Richen Liu, Lingyu Sun, Xuefeng Huang, Yiran Li, Jiang Zhang, Siru Chen, Zhouhao Wu, Ayush Kumar, Chufan Lai

2025Year

Abstract

Interactive data illustrations in an immersive environment are challenging due to their inherent ambiguities during the interaction. These challenges are introduced by visual clutter and 3D occlusions resulting from depth information, as well as the relatively inefficient fine-grained manipulations required by handle controllers on immersive devices. In this paper, we propose Meta-Illustrator, an illustration transfer tool to generate immersive 3D illustrations for a volumetric data with the 2D illustrated results transferred from its one or multiple 2D slices (images). Initially, the slices can be illustrated by users expressively, owing to the plenty of the existing mature 2D sketching techniques and image processing algorithms. Then the 2D illustrated results on the slices can be intelligently transferred from their 2D image space to 3D volumetric space by Meta-Illustrator. Compared to the state-of-the-art image-to-image style transfer neural networks, which are either computation-intensive or memory-intensive, the proposed 2D-to-3D transferring approach can be built on a desktop PC without training. We demonstrate the usability, expressiveness, and effectiveness of Meta-Illustrator by both quantitative and qualitative evaluations.

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