no code implementations • CVPR 2025 • Ji Woo Hong, Tri Ton, Trung X. Pham, Gwanhyeong Koo, Sunjae Yoon, Chang D. Yoo
This paper introduces ITA-MDT, the Image-Timestep-Adaptive Masked Diffusion Transformer Framework for Image-Based Virtual Try-On (IVTON), designed to overcome the limitations of previous approaches by leveraging the Masked Diffusion Transformer (MDT) for improved handling of both global garment context and fine-grained details.
no code implementations • 31 Oct 2024 • Sunjae Yoon, Gwanhyeong Koo, Younghwan Lee, Chang D. Yoo
The TPC provides a calibrated reference image for the diffusion model, enhancing its capability to understand the correspondence between human shapes in the reference and target images.
1 code implementation • 25 Jul 2024 • Gwanhyeong Koo, Sunjae Yoon, Ji Woo Hong, Chang D. Yoo
Current image editing methods primarily utilize DDIM Inversion, employing a two-branch diffusion approach to preserve the attributes and layout of the original image.
1 code implementation • 10 Jun 2024 • Sunjae Yoon, Gwanhyeong Koo, Geonwoo Kim, Chang D. Yoo
In video editing, the hallmark of a quality edit lies in its consistent and unobtrusive adjustment.
no code implementations • 18 Jan 2024 • Gwanhyeong Koo, Sunjae Yoon, Chang D. Yoo
To address this, we introduce an innovative method that maintains the principles of the NTI while accelerating the image editing process.
no code implementations • 10 Dec 2023 • Sunjae Yoon, Gwanhyeong Koo, Ji Woo Hong, Chang D. Yoo
To this end, this paper proposes Neutral Editing (NeuEdit) framework to enable complex non-rigid editing by changing the motion of a person/object in a video, which has never been attempted before.
no code implementations • ICCV 2023 • Sunjae Yoon, Gwanhyeong Koo, Dahyun Kim, Chang D. Yoo
These proposals are assumed to contain many distinguishable scenes in a video as candidates.