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Jiyoung Kim

7 accepted papers

2026

MATRIX: Mask Track Alignment for Interaction-aware Video Generation

ICLR 2026poster

Video DiTs have advanced video generation, yet they still struggle to model multi-instance or subject-object interactions. This raises a key question: How do these models internally represent interactions? To answer this, we curate MATRIX-11K, a video dataset with interaction-aware captions and mul…

Cited by 0SourcecodeScholar
2025

AM-Adapter: Appearance Matching Adapter for Exemplar-based Semantic Image Synthesis in-the-Wild

ICCV 2025poster

Exemplar-based semantic image synthesis generates images aligned with semantic content while preserving the appearance of an exemplar. Conventional structure-guidance models like ControlNet, are limited as they rely solely on text prompts to control appearance and cannot utilize exemplar images as i…

Cited by 0SourcePDFScholar
2025

Emergent Temporal Correspondences from Video Diffusion Transformers

NeurIPS 2025poster

Recent advancements in video diffusion models based on Diffusion Transformers (DiTs) have achieved remarkable success in generating temporally coherent videos. Yet, a fundamental question persists: how do these models internally establish and represent temporal correspondences across frames? We int…

Cited by 0SourceScholar
2025

K/DA: Automated Data Generation Pipeline for Detoxifying Implicitly Offensive Language in Korean

ACL 2025long

Language detoxification involves removing toxicity from offensive language. While a neutral-toxic paired dataset provides a straightforward approach for training detoxification models, creating such datasets presents several challenges: i) the need for human annotation to build paired data, and ii)…

2025

MoDiTalker: Motion-Disentangled Diffusion Model for High-Fidelity Talking Head Generation

AAAI 2025technical

Conventional GAN-based models for talking head generation often suffer from limited quality and unstable training. Recent approaches based on diffusion models have attempted to address these limitations and improve fidelity. However, they still face challenges, such as intensive sampling times and d…

2024

Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization

AAAI 2024technical

Unsupervised semantic segmentation (USS) aims to discover and recognize meaningful categories without any labels. For a successful USS, two key abilities are required: 1) information compression and 2) clustering capability. Previous methods have relied on feature dimension reduction for informatio…

Cited by 1SourcePDFScholar
2023

Depth-Relative Self Attention for Monocular Depth Estimation

IJCAI 2023poster

Monocular depth estimation is very challenging because clues to the exact depth are incomplete in a single RGB image. To overcome the limitation, deep neural networks rely on various visual hints such as size, shade, and texture extracted from RGB information. However, we observe that if such hints…

Cited by 5SourcePDFScholar