James Hun Kim

I am an AI Researcher & Engineer in the Visual AI Group at Samsung Electronics (MX), and an M.S. student in the Interdisciplinary Program in Artificial Intelligence (IPAI) at Seoul National University, advised by Prof. Se Young Chun (ICL).

My research focuses on differentiable rendering, generative models, and 3D vision, with a particular emphasis on optimization-driven systems that bridge research with large-scale production. At Samsung, I led the end-to-end development of a range of AI-powered camera features, from model design to commercialization, deploying real-time vision systems across smartphones, tablets, and foldable devices under strict latency, memory, and power constraints. I am particularly interested in developing efficient and scalable AI systems that translate advanced research into production.

I am a Korean-American. I publish under my Korean name, Junghun James Kim, and also go by my English name, James Hun Kim.

James Hun Kim

News

Selected Publications

* denotes equal contribution.  See all publications →

2026

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HyFL-CLIP: Hyperbolic Fine-Tuning of CLIP for Robust Long-Context Understanding

Ji Ha Jang*, Hayeon Kim*, Chulwon Lee, Junghun James Kim, Se Young Chun

European Conference on Computer Vision (ECCV), 2026

ECCV Vision-Language

A hyperbolic fine-tuning framework that distills the well-established image-text alignment of Euclidean CLIP into hyperbolic space via cross-manifold similarity distillation.

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Human Interaction-Aware 3D Reconstruction from a Single Image

Gwanghyun Kim*, Junghun James Kim*, Suh Yoon Jeon*, Jason Park, Se Young Chun

Conference on Computer Vision and Pattern Recognition (CVPR), 2026  (Highlight)

CVPR Highlight Co-1st Author 3D Vision

A holistic framework for reconstructing physically plausible, high-fidelity textured 3D humans from a single image, explicitly modeling group- and instance-level cues to handle perspective distortion, occlusion, and inter-human interactions.

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DiffBMP: Differentiable Rendering with Bitmap Primitives

Seongmin Hong*, Junghun James Kim*, Daehyeop Kim, Insoo Chung, Se Young Chun

Conference on Computer Vision and Pattern Recognition (CVPR), 2026

CVPR Co-1st Author Differentiable Rendering

A scalable differentiable renderer for bitmap primitives that optimizes thousands of elements via a highly parallelized custom CUDA pipeline, enabling practical image/video composition, layered export, and artist-friendly creative workflows.

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UNCHA: Uncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models

Hayeon Kim*, Ji Ha Jang*, Junghun James Kim, Se Young Chun

Conference on Computer Vision and Pattern Recognition (CVPR), 2026  (Highlight)

CVPR Highlight Vision-Language

An uncertainty-guided hyperbolic vision-language framework that models part-to-whole semantic representativeness via adaptive uncertainty, improving hierarchical compositional understanding and performance on zero-shot classification, retrieval, and multi-label classification.

Experience

Patents

Education