2 citations · 2 across the 6 of their papers we have counts for
7 papers
ShoeFit: A New Dataset and Dual-image-stream DiT Framework for Virtual Footwear Try-On
Yuhan Li, Zhiyu Jin, Yifan Tong +5
Virtual footwear try-on (VFTON), a critical yet underexplored area in virtual try-on (VTON), aims to synthesize faithful try-on results given diverse footwear and model images whil…
RAGDiffusion++: From Macro-Retrieval to Micro-Fidelity Alignment for Garment Generation
Yuhan Li, Xianfeng Tan, Fangao Zeng +6
Standard clothing asset generation---restoring forward-facing flat-lay garment images from diverse real-world contexts---holds immense commercial value yet demands both macroscopic…
simpleposter: A simple baseline for product poster generation
Benlei Cui, Fangao Zeng, Weitao Jiang +6
Product poster generation poses distinct challenges beyond general poster design, requiring both faithful preservation of product appearance and precise control over dense, multi-l…
Erase Diffusion: Empowering Object Removal Through Calibrating Diffusion Pathways
Yi Liu, Hao Zhou, Wenxiang Shang +2
Erase inpainting, or object removal, aims to precisely remove target objects within masked regions while preserving the overall consistency of the surrounding content. Despite diff…
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation
Xianfeng Tan, Yuhan Li, Wenxiang Shang +6
Standard clothing asset generation involves restoring forward-facing flat-lay garment images displayed on a clear background by extracting clothing information from diverse real-wo…
TextMaster: A Unified Framework for Realistic Text Editing via Glyph-Style Dual-Control
Zhenyu Yan, Jian Wang, Aoqiang Wang +3
In image editing tasks, high-quality text editing capabilities can significantly reduce both human and material resource costs. Existing methods, however, face significant limitati…
AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario
Yuhan Li, Hao Zhou, Wenxiang Shang +3
While image-based virtual try-on has made significant strides, emerging approaches still fall short of delivering high-fidelity and robust fitting images across various scenarios,…