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20242026
most citedRepresentation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

4 citations · 6 across the 15 of their papers we have counts for

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cs.CV2026

SSAFE: Simple and Strong AI-Generated Image Detection via Frozen Vision Encoders

Seunghyun Lee, Byoungkwon Kim, Jaehyun Nam +2

The rapid advancement of generative models has blurred the boundary between synthetic and real imagery, creating an urgent need for reliable deepfake detection. Yet most existing a…

cs.CV2025

Decoupled MeanFlow: Turning Flow Models into Flow Maps for Accelerated Sampling

Kyungmin Lee, Sihyun Yu, Jinwoo Shin

Denoising generative models, such as diffusion and flow-based models, produce high-quality samples but require many denoising steps due to discretization error. Flow maps, which es…

cs.CV2025

Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model

John Won, Kyungmin Lee, Huiwon Jang +2

Augmenting vision-language-action models (VLAs) with world models is promising for robotic policy learning but faces challenges in jointly predicting states and actions due to the…

cs.CV2025

Improving Motion in Image-to-Video Models via Adaptive Low-Pass Guidance

June Suk Choi, Kyungmin Lee, Sihyun Yu +3

Recent text-to-video (T2V) models have demonstrated strong capabilities in producing high-quality, dynamic videos. To improve the visual controllability, recent works have consider…

cs.CV2025

Calibrated Multi-Preference Optimization for Aligning Diffusion Models

Kyungmin Lee, Xiaohang Li, Qifei Wang +7

Aligning text-to-image (T2I) diffusion models with preference optimization is valuable for human-annotated datasets, but the heavy cost of manual data collection limits scalability…

cs.CV2024★ 4 cited

Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Sihyun Yu, Sangkyung Kwak, Huiwon Jang +4

Recent studies have shown that the denoising process in (generative) diffusion models can induce meaningful (discriminative) representations inside the model, though the quality of…

cs.CV2024

DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing

June Suk Choi, Kyungmin Lee, Jongheon Jeong +3

Recent advances in diffusion models have introduced a new era of text-guided image manipulation, enabling users to create realistic edited images with simple textual prompts. Howev…

cs.CV2024

DreamFlow: High-Quality Text-to-3D Generation by Approximating Probability Flow

Kyungmin Lee, Kihyuk Sohn, Jinwoo Shin

Recent progress in text-to-3D generation has been achieved through the utilization of score distillation methods: they make use of the pre-trained text-to-image (T2I) diffusion mod…

cs.CV2024★ 2 cited

Improving Diffusion Models for Authentic Virtual Try-on in the Wild

Yisol Choi, Sangkyung Kwak, Kyungmin Lee +2

This paper considers image-based virtual try-on, which renders an image of a person wearing a curated garment, given a pair of images depicting the person and the garment, respecti…

cs.CV2024

Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models

Kyungmin Lee, Sangkyung Kwak, Kihyuk Sohn +1

Text-to-image (T2I) diffusion models, when fine-tuned on a few personal images, can generate visuals with a high degree of consistency. However, such fine-tuned models are not robu…