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20222026
most citedDeep Cross-Modal Steganography Using Neural Representations

1 citations · 2 across the 11 of their papers we have counts for

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cs.CV2026

Learning Neural Deformation Representation for 4D Dynamic Shape Generation

Gyojin Han, Jiwan Hur, Jaehyun Choi +1

Recent developments in 3D shape representation opened new possibilities for generating detailed 3D shapes. Despite these advances, there are few studies dealing with the generation…

cs.CV2026

Cross-Axis Feature Fusion with Joint-Wise Motion Difference Prediction for Text-Based 3D Human Motion Editing

Gyojin Han, Junmo Kim

We address text-based 3D human motion editing, where the goal is to preserve the style and structure of a source motion while applying edits described in natural language. The rele…

cs.CV2025

B4DL: A Benchmark for 4D LiDAR LLM in Spatio-Temporal Understanding

Changho Choi, Youngwoo Shin, Gyojin Han +2

Understanding dynamic outdoor environments requires capturing complex object interactions and their evolution over time. LiDAR-based 4D point clouds provide precise spatial geometr…

cs.CV2025

DAM: Domain-Aware Module for Multi-Domain Dataset Condensation

Jaehyun Choi, Gyojin Han, Dong-Jae Lee +2

Dataset Condensation (DC) has emerged as a promising solution to mitigate the computational and storage burdens associated with training deep learning models. However, existing DC…

cs.CV2025

PRISM: Video Dataset Condensation with Progressive Refinement and Insertion for Sparse Motion

Jaehyun Choi, Jiwan Hur, Gyojin Han +2

Video dataset condensation aims to reduce the immense computational cost of video processing. However, it faces a fundamental challenge regarding the inseparable interdependence be…

cs.CV2024

Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance

Jiwan Hur, Dong-Jae Lee, Gyojin Han +3

Masked generative models (MGMs) have shown impressive generative ability while providing an order of magnitude efficient sampling steps compared to continuous diffusion models. How…

cs.CV2023

Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning

Jiwan Hur, Jaehyun Choi, Gyojin Han +2

Training diffusion models on limited datasets poses challenges in terms of limited generation capacity and expressiveness, leading to unsatisfactory results in various downstream t…