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Pengcheng Xu

7 accepted papers

2026

The devil is in the details: Enhancing Video Virtual Try-On via Keyframe-Driven Details Injection

CVPR 2026

Although diffusion transformer (DiT)-based video virtual try-on (VVT) has made significant progress in synthesizing realistic videos, existing methods still struggle to capture fine-grained garment dynamics and preserve background integrity across video frames. They also incur high computational cos

Cited by 0SourceScholar
2025

Plug-and-Play Tri-Branch Invertible Block for Image Rescaling

AAAI 2025technical

High-resolution (HR) images are commonly downscaled to low-resolution (LR) to reduce bandwidth, followed by upscaling to restore their original details. Recent advancements in image rescaling algorithms have employed invertible neural networks (INNs) to create a unified framework for downscaling and…

2025

Textualize Visual Prompt for Image Editing via Diffusion Bridge

AAAI 2025technical

Visual prompt, a pair of before-and-after edited images, can convey indescribable imagery transformations and prosper in image editing. However, current visual prompt methods rely on a pretrained text-guided image-to-image generative model that requires a triplet of text, before, and after images fo…

Cited by 0SourcePDFScholar
2025

Unveil Inversion and Invariance in Flow Transformer for Versatile Image Editing

CVPR 2025poster

Leveraging the large generative prior of the flow transformer for tuning-free image editing requires authentic inversion to project the image into the model's domain and a flexible invariance control mechanism to preserve non-target contents. However, the prevailing diffusion inversion performs defi…

Cited by 3SourcePDFScholar
2023

Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation

AAAI 2023technical

Current methods of blended targets domain adaptation (BTDA) usually infer or consider domain label information but underemphasize hybrid categorical feature structures of targets, which yields limited performance, especially under the label distribution shift. We demonstrate that domain labels are n…

2023

When Source-Free Domain Adaptation Meets Learning with Noisy Labels

ICLR 2023top-25%

Recent state-of-the-art source-free domain adaptation (SFDA) methods have focused on learning meaningful cluster structures in the feature space, which have succeeded in adapting the knowledge from source domain to unlabeled target domain without accessing the private source data. However, existing…

Cited by 57SourcePDFScholar