Order Matters: 3D Shape Generation from Sequential VR Sketches
Yizi Chen, Sidi Wu, Tianyi Xiao, Nina Wiedemann, Loic Landrieu
Abstract
VR sketching lets users explore and iterate on ideas directly in 3D, offering a faster and more intuitive alternative to conventional CAD software. However, existing sketch-to-shape models ignore the temporal ordering of strokes, discarding crucial cues about structure and design intent. We introduce VRSketch2Shape, the first framework and multi-category dataset for 3D shape generation from sequential VR sketches. Our contributions are threefold: (i) an automated pipeline that generates ordered VR sketches from arbitrary shapes, (ii) a dataset comprising over 20k synthetic and 900 hand-drawn sketch-shape pairs across four categories, and (iii) an order-aware sketch encoder coupled with a diffusion-based 3D generator. Our approach yields higher geometric fidelity than prior work and generalizes effectively from synthetic to real sketches with minimal supervision. All data and models are released open-source on on https://chenyizi086.github.io/VRSketch2Shape_website/.
BibTeX
@inproceedings{cvpr2026_ordermatters3dsh,
title = {Order Matters: 3D Shape Generation from Sequential VR Sketches},
author = {Yizi Chen and Sidi Wu and Tianyi Xiao and Nina Wiedemann and Loic Landrieu},
booktitle = {CVPR 2026},
year = {2026}
}