DeepTreeSketch: Neural Graph Prediction for Faithful 3D Tree Modeling from Sketches

要旨

We present DeepTreeSketch, a novel AI-assisted sketching system that enables users to create realistic 3D tree models from 2D freehand sketches. Our system leverages a tree graph prediction network, TGP-Net, to learn the underlying structural patterns of trees from a large collection of 3D tree models. The TGP-Net simulates the iterative growth of botanical trees and progressively constructs the 3D tree structures in a bottom-up manner. Furthermore, our system supports a flexible sketching mode for both precise and coarse control of the tree shapes by drawing branch strokes and foliage strokes, respectively. Combined with a procedural generation strategy, users can freely control the foliage propagation with diverse and fine details. We demonstrate the expressiveness, efficiency, and usability of our system through various experiments and user studies. Our system offers a practical tool for 3D tree creation, especially for natural scenes in games, movies, and landscape applications.

著者
Zhihao Liu
The University of Tokyo, Tokyo, Japan
Yu LI
Chinese Academy of Siences., Shenzhen, China
Fangyuan Tu
The Chinese University of Hong Kong, Hong Kong, China
Ruiyuan Zhang
The Chinese University of Hong Kong Shenzhen, Shenzhen, China
Zhanglin Cheng
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Naoto Yokoya
The University of Tokyo, Tokyo, Japan
論文URL

doi.org/10.1145/3613904.3642125

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Remote Presentations: Highlight on AI

Remote Sessions
14 件の発表
2024-05-13 18:00:00
2024-05-14 02:20:00