My research interest mainly focuses on Computer Graphics, especially geometric modeling,
3D shape generation and manipulation.
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DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement
Qimin Chen,
Zhiqin Chen,
Vladimir G. Kim,
Noam Aigerman,
Hao Zhang,
Siddhartha Chaudhuri
ECCV 2024
pdf /
code /
project page
DECOLLAGE is a learning-based method that enables novice users to add geometric details to a coarse 3D shape by selecting regions on it and
assigning them the styles of exemplar shapes with compelling geometric details.
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DAE-Net: Deforming Auto-Encoder for fine-grained shape co-segmentation
Zhiqin Chen,
Qimin Chen,
Hang Zhou,
Hao Zhang
ACM SIGGRAPH 2024 (Conference)
pdf /
code
DAE-Net is an unsupervised 3D shape co-segmentation method that learns a set of deformable part templates from a shape collection, which
yields high-quality, consistent, and fine-grained 3D shape co-segmentation.
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ShaDDR: Interactive Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering
Qimin Chen,
Zhiqin Chen,
Hang Zhou,
Hao Zhang
ACM SIGGRAPH Asia 2023 (Conference)
pdf /
code /
project page
The first example-based deep generative neural network for generating a high-resolution textured 3D shape through geometry detailization
and conditional texture generation applied to an input coarse voxel shape.
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D2CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts
Fenggen Yu,
Qimin Chen,
Maham Tanveer,
Ali Mahdavi-Amiri,
Hao Zhang
NeurIPS 2023
arXiv
D2CSG is a neural model composed of two dual and complementary network branches, with dropouts, for unsupervised
learning of compact constructive solid geometry (CSG) representations of 3D CAD shapes.
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UNIST: Unpaired Neural Implicit Shape Translation Network
Qimin Chen,
Johannes Merz,
Aditya Sanghi,
Hooman Shayani,
Ali Mahdavi-Amiri,
Hao Zhang
CVPR 2022
pdf /
supplementary /
code /
project page
The first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains.
UNIST can learn both style-preserving content alteration and content-preserving style transfer.
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A New Deep Learning Engine for CoralNet
Qimin Chen,
Oscar Beijbom,
Stephen Chan,
Jessica Bouwmeester,
David Kriegman
ICCV Workshop on Computer Vision in the Ocean
(ICCVW), 2021
pdf /
bibtex /
coralnet /
code
CoralNet is a cloud-based website and platform for manual, semi-automatic and automatic analysis of coral
reef images. Users access CoralNet through optimized web-based workflows for common tasks, other systems
can interface through API.
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Topology-Aware Single-Image 3D Shape Reconstruction
Qimin Chen,
Vincent Nguyen,
Feng Han,
Raimondas Kiveris,
Zhuowen Tu
CVPR Workshop on Learning 3D Generative Models
(CVPRW), 2020
pdf /
poster /
bibtex
Composing volumetric-based generative model with topology-awareness auto-encoder allows them to learn
high-level topological properties such as genus and connectivity for 3D shape reconstruction.
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Adobe Internship
Seattle [May - Aug, 2024]
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Adobe Internship
Seattle [May - Aug, 2023]
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TA - CMPT 762: Computer Vision
SFU [Spring 22]
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TA - CMPT 464/764: Geometric Modeling in Computer Graphics
SFU [Fall 21]
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Tutor - CSE 152: Introduction to Computer Vision
UCSD [Spring 19]
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Transactions on Graphics (TOG), SIGGRAPH Asia 2024, SIGGRAPH Asia 2023, CVPR 2024, CVPR 2023, ECCV 2024, AAAI 2024, Eurographics 2025
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Layout inspired by Jon Barron
. Thank you Jon. © 2024
Deep Learning and Keep Learning.
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