Qimin Chen

Qimin Chen

I am a Ph.D. student in GrUVi lab of School of Computing Science at Simon Fraser University, under the supervision of Prof. Hao (Richard) Zhang.

Before that, I received my M.S. degree in Computational Science from UC San Diego, where I worked at Center for Visual Computing Lab advised by Prof. David J. Kriegman. I also worked at Machine Learning, Perception, and Cognition Lab (mlPC) advised by Prof. Zhuowen Tu. Before that, I received my B.S degree in Computer Science and Technology from Fuzhou University.

qca43 at sfu dot ca / Resume / LinkedIn / Google Scholar / Github

AI Conference Deadlines

CVF Open Access

3D Shape Analysis Paper List
Neural Rendering Paper List

Research

My research focuses on geometry modeling, machine learning, and shape analysis. I am particularly interested in building generative models for fine-grained, controllable 3D shape synthesis guided by geometry, images, or text.

	GenVDM: Generating Vector Displacement Maps From a Single Image Yuezhi Yang, Qimin Chen, Vladimir G. Kim, Siddhartha Chaudhuri, Qixing Huang, Zhiqin Chen CVPR 2025 (highlight) pdf / code / project page GenVDM is the first method for generating Vector Displacement Maps (VDMs): parameterized, detailed geometric stamps commonly used in 3D modeling. It generates multi-view normal maps from a single input image and then reconstructs a VDM from the normals via a novel reconstruction pipeline.
	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.
	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.
	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.
	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.
	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.
	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.
	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.

Working experience

Adobe Reseach Scientist Intern Seattle [May - Aug, 2024]
Adobe Reseach Scientist Intern Seattle [May - Aug, 2023]

Teaching

TA - CMPT 420/728: Deep Learning SFU [Spring 25]
TA - CMPT 762: Computer Vision SFU [Spring 22]
TA - CMPT 464/764: Geometric Modeling in Computer Graphics SFU [Fall 21]
Tutor - CSE 152: Introduction to Computer Vision UCSD [Spring 19]

Reviewer

Transactions on Graphics (TOG), SIGGRAPH 2025, SIGGRAPH Asia 2024, SIGGRAPH Asia 2023, CVPR 2025 (Outstanding Reviewer), CVPR 2024, CVPR 2023, ICCV 2025, ECCV 2024, AAAI 2024, Eurographics 2025