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Fangyuan Tu

dblp:374/9381 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2024
0000-0001-5037-4391ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%
Artificial intelligence
1 paper
Graph learning · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing › shape modeling
sketch-based modeling
0.812024
DeepTreeSketch: Neural Graph Prediction for Faithful 3D Tree Modeling from Sketches · CHI 2024
Geometric modeling and processing › procedural modeling
tree modeling
0.812024
DeepTreeSketch: Neural Graph Prediction for Faithful 3D Tree Modeling from Sketches · CHI 2024

Methods — techniques the papers use, named apart from their topics

procedural generation · 1.5neural graph prediction · 1.5
YearPublicationVenuePosition
2024 DeepTreeSketch: Neural Graph Prediction for Faithful 3D Tree Modeling from Sketches
abstract
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.
Fangyuan Tu, Ruiyuan Zhang, Zhanglin Cheng, Naoto Yokoya
CHI3
2024 VRTree: Example-Based 3D Interactive Tree Modeling in Virtual Reality
abstract
Abstract We present VRTree, an example‐based interactive virtual reality (VR) system designed to efficiently create diverse 3D tree models while faithfully preserving botanical characteristics of real‐world references. Our method employs a novel representation called Hierarchical Branch Lobe (HBL), which captures the hierarchical features of trees and serves as a versatile intermediary for intuitive VR interaction. The HBL representation decomposes a 3D tree into a series of concise examples, each consisting of a small set of main branches, secondary branches, and lobe‐bounded twigs. The core of our system involves two key components: (1) We design an automatic algorithm to extract an initial library of HBL examples from real tree point clouds. These HBL examples can be optionally refined according to user intentions through an interactive editing process. (2) Users can interact with the extracted HBL examples to assemble new tree structures, ensuring the local features align with the target tree species. A shape‐guided procedural growth algorithm then transforms these assembled HBL structures into highly realistic, finegrained 3D tree models. Extensive experiments and user studies demonstrate that VRTree outperforms current state‐of‐the‐art approaches, offering a highly effective and easy‐to‐use VR tool for tree modeling.
Di Wu 0074, Mingxin Yang, Fangyuan Tu, Zhanglin Cheng
Comput. Graph. Forum4