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20222026
most citedLeveraging Off-the-shelf Diffusion Model for Multi-attribute Fashion Image Manipulation

5 citations · 11 across the 6 of their papers we have counts for

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cs.CV2024

Fashion Style Editing with Generative Human Prior

Chaerin Kong, Seungyong Lee, Soohyeok Im +1

Image editing has been a long-standing challenge in the research community with its far-reaching impact on numerous applications. Recently, text-driven methods started to deliver p…

cs.CV2023

ConcatPlexer: Additional Dim1 Batching for Faster ViTs

Donghoon Han, Seunghyeon Seo, Donghyeon Jeon +3

Transformers have demonstrated tremendous success not only in the natural language processing (NLP) domain but also the field of computer vision, igniting various creative approach…

cs.CV20233 cited

AADiff: Audio-Aligned Video Synthesis with Text-to-Image Diffusion

Seungwoo Lee, Chaerin Kong, Donghyeon Jeon +1

Recent advances in diffusion models have showcased promising results in the text-to-video (T2V) synthesis task. However, as these T2V models solely employ text as the guidance, the…

cs.CV20225 cited

Leveraging Off-the-shelf Diffusion Model for Multi-attribute Fashion Image Manipulation

Chaerin Kong, DongHyeon Jeon, Ohjoon Kwon +1

Fashion attribute editing is a task that aims to convert the semantic attributes of a given fashion image while preserving the irrelevant regions. Previous works typically employ c…

cs.CV20222 cited

Towards Efficient Neural Scene Graphs by Learning Consistency Fields

Yeji Song, Chaerin Kong, Seoyoung Lee +2

Neural Radiance Fields (NeRF) achieves photo-realistic image rendering from novel views, and the Neural Scene Graphs (NSG) \cite{ost2021neural} extends it to dynamic scenes (video)…