Matiur Minar is a computer vision researcher and AI/ML engineer with 11 years of experience building production-grade deep learning systems across academia and industry. He has driven research in 3D vision and generative models at top Korean labs and implemented practical solutions—from CVPRW virtual try-on codebases to LLM-powered virtual mall assistants and geospatial digital twins for smart cities. His work spans end-to-end deployment: dataset curation and model architecture tweaks to REST API integration and SLA-driven telecom systems that served tens of millions with 99.99% uptime. He has a strong track record in translating research into commercial impact in fashion, healthcare drug-discovery, and urban infrastructure across Asia and the Gulf. Currently a Graduate Research Assistant at Sogang University and incoming PhD student, he combines rigorous academic output with hands-on engineering and notable open-source contributions to CP-VTON+. Fluent in cross-domain problem solving, he brings both systems-level reliability and experimental creativity to complex AI projects.
11 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science and Engineering, Doctor of Philosophy - PhD, Computer Science and Engineering at Sogang University
Master's degree, Electrical and Information Engineering, Master's degree, Electrical and Information Engineering at Seoul National University of Science and Technology
Bachelor of Science (BSc), Computer Science and Engineering, Bachelor of Science (BSc), Computer Science and Engineering at Bangladesh University of Engineering and Technology
Official implementation for "CP-VTON+: Clothing Shape and Texture Preserving Image-Based Virtual Try-On", CVPRW 2020
Role in this project:
ML Engineer
Contributions:2 reviews, 75 commits, 4 PRs in 2 years 5 months
Contributions summary:Matiur's contributions center around modifying and refining the code for a virtual try-on system (CP-VTON+). Their work includes adjustments to dataset processing, specifically related to segmentation and body masking, along with modifications to the testing and training scripts. Code changes encompass updates to data paths, modifications to model architectures, and adjustments to the loss functions, indicating a focus on model training and evaluation.
Contributions:62 commits, 2 PRs, 54 pushes in 1 year 4 months
fcnclothingparsingu-netfashion
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