Hee Jae Kim

I am a third-year Ph.D. student at Boston University, advised by Prof. Eshed Ohn-Bar. My research interests lie in computer vision, robotics, and machine learning with their applications in autonomous and assistive systems.

Prior to BU, I got my master's degree (2019-2021) at the Ewha Womans University, where I worked with Prof. Byung-Uk Lee and Prof. Jewon Kang, on multi-view 360-degree videos.

In 2018, I worked as a research intern at the Artificial Intelligence Research Center at ETRI (Korea). In 2021, I worked as a researcher at RainbirdGEO.

Email  /  Scholar  /  Github  /  CV

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Research

Text to Blind Motion
Hee Jae Kim, Kathakoli Sengupta, Masaki Kuribayashi, Hernisa Kaccori, and Eshed Ohn-Bar
Neural Information Processing Systems (NeurIPS), 2024
project page / paper

We introduce BlindWays, the first multimodal 3D human motion benchmark for pedestrians who are blind, featuring data from 11 participants (varying in gender, age, visual acuity, onset of disability, mobility aid use, and navigation habits) in an outdoor navigation study. We provide rich two-level textual descriptions informed by third-person and egocentric videos. We benchmark state-of-the-art 3D human prediction models, finding poor performance with off-the-shelf and pretraining-based methods for our novel task.

Motion Diversification Networks
Hee Jae Kim and Eshed Ohn-Bar
Computer Vision and Pattern Recognition (CVPR), 2024
project page / paper / code / poster

We introduce Motion Diversification Networks, a novel framework for learning to generate realistic and diverse 3D human motion. Towards more realistic and functional 3D motion models, this work uncovers limitations in existing generative modeling techniques, particularly in overly simplistic latent code sampling strategies.

Unsupervised Clustering of Geostationary Satellite Cloud Properties for Estimating Precipitation Probabilities of Tropical Convective Clouds
Doyi Kim, Hee Jae Kim, and Yong-Sang Choi
Journal of Applied Meteorology and Climatology (JAMC), 2023
paper

This study aims to explore the cloud properties of tropical convective clouds (TCC) that indicate a high probability of precipitation by training a neural network with daytime satellite imagery.

360° Image Reference-Based Super-Resolution using Latitude-Aware Convolution Learned from Synthetic to Real
Hee Jae Kim, Jewon Kang, and Byung-Uk Lee
IEEE Access, 2021
project page / paper / code

We propose an efficient reference-based 360° image super-resolution (RefSR) technique to exploit a wide field of view (FoV) among adjacent 360° cameras. We do not assume any structured camera arrays but use a reference image captured in an arbitrary viewpoint. Accordingly, we develop a long-range 360 disparity estimator (DE360) to overcome a large and distorted disparity between equirectangular projection (ERP) images, particularly near the poles.