Jiin Im

Jiin Im

Research Assistant @ Spatial AI Lab, Hanyang University

About

I am a Research Assistant at the Spatial AI Lab, Hanyang University, advised by Prof. Je Hyeong Hong. My research focuses on spatial and geometric understanding in computer vision and graphics. I am particularly interested in how visual systems discover and represent structure across images, objects, and 3D shapes, with experience in correspondence, shape analysis, and geometry-aware representation learning.

My current work studies correspondence as a way to reason about relational structure across visual and geometric data. I have explored both image-level and shape-level matching, investigating how geometric constraints can ground feature correspondence beyond surface appearance, and how consistent maps can reveal spatial and functional relationships between parts, objects, and shapes.

I aim to extend this direction toward scene-level spatial reasoning and 3D representation learning more broadly. Correspondence offers a natural lens for understanding what structure transfers across instances and environments. I believe that representing and preserving geometric, physical, and topological priors is central to learning, generation, and generalization in 3D.

I am actively seeking Ph.D. opportunities starting in Fall 2027. I would be very happy to discuss my research interests or potential collaborations over email or a brief Zoom call! ☕

Education

Hanyang University Mar. 2026 - Present

Research Assistant (Advisor: Je Hyeong Hong)

Hanyang University Mar. 2024 - Feb. 2026

M.S. in Electronic Engineering (Advisor: Je Hyeong Hong)

Hanyang University Mar. 2020 - Feb. 2024

B.S. in Electronic Engineering, Summa Cum Laude

Publications

Chebyshev Differential Flows Teaser Image

Chebyshev Differential Flows for Shape Matching and Interpolation

Jiin Im, Sisung Liu, Je Hyeong Hong

Under review

Shape-of-You Teaser Image

Shape-of-You: Fused Gromov-Wasserstein Optimal Transport for Semantic Correspondence in-the-Wild

Jiin Im, Sisung Liu, Je Hyeong Hong

CVPR 2026

Paper Code
FUN-AD Teaser Image

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data

Jiin Im*, Yongho Son*, Je Hyeong Hong

WACV 2025

Paper Code

Projects

Semantic Correspondence via Structural Information on 2D and 3D Domains Mar. 2025 - Present

Establishing semantic correspondence on both 2D image and 3D geometry domains using structural information, which is related to Shape-of-You.

Non-Rigid Registration of Deformed Point Clouds via Geometric Correspondence Jun. 2025 - Dec. 2025

Focused on estimating geometric correspondence in deformed point clouds to perform robust non-rigid registration.

Privacy-Preserving Geometric Description against Image Reconstruction Jun. 2024 - Feb. 2025

Explored geometric description methods that prevent original image recovery from descriptors while maintaining matching performance.

Unsupervised Anomaly Detection with Noisy Training Data Jan. 2023 - May. 2024

Addressing anomaly detection in unsupervised settings with noisy training data related to FUN-AD.

Academic Service

Experience

Hobbies

In my free time, I enjoy:

Contact

✉️ Email: skqldit33@hanyang.ac.kr

📍 Location: Engineering Center Annex Unit 415-1, Hanyang University, Seongdong-gu, Wangsimni-ro 222, Seoul, South Korea