most citedNoise Map Guidance: Inversion with Spatial Context for Real Image Editing

1 citations · 2 across the 5 of their papers we have counts for

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5 papers

cs.CV2026

SEAL: Semantic-aware Single-image Sticker Personalization with a Large-scale Sticker-tag Dataset

Changhyun Roh, Yonghyun Jeong, Jonghyun Lee +2

Synthesizing a target concept from a single reference image is challenging in diffusion-based personalized text-to-image generation, particularly for sticker personalization where…

cs.CV2026

See and Fix the Flaws: Enabling VLMs and Diffusion Models to Comprehend Visual Artifacts via Agentic Data Synthesis

Jaehyun Park, Minyoung Ahn, Minkyu Kim +3

Despite recent advances in diffusion models, AI generated images still often contain visual artifacts that compromise realism. Although more thorough pre-training and bigger models…

cs.CV2024

One-Shot Structure-Aware Stylized Image Synthesis

Hansam Cho, Jonghyun Lee, Seunggyu Chang +1

While GAN-based models have been successful in image stylization tasks, they often struggle with structure preservation while stylizing a wide range of input images. Recently, diff…

cs.CV20241 cited

Noise Map Guidance: Inversion with Spatial Context for Real Image Editing

Hansam Cho, Jonghyun Lee, Seoung Bum Kim +2

Text-guided diffusion models have become a popular tool in image synthesis, known for producing high-quality and diverse images. However, their application to editing real images o…

cs.CV20241 cited

Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis

Jonghyun Lee, Hansam Cho, Youngjoon Yoo +2

Addressing the limitations of text as a source of accurate layout representation in text-conditional diffusion models, many works incorporate additional signals to condition certai…