Search Results for author:
Found 7 papers, 4 papers with code
Fashion Matrix: Editing Photos by Just Talking
The utilization of Large Language Models (LLMs) for the construction of AI systems has garnered significant attention across diverse fields.
GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning
Specifically, compared with the previous global warping mechanism, LFGP employs local flows to warp garments parts individually, and assembles the local warped results via the global garment parsing, resulting in reasonable warped parts and a semantic-correct intact garment even with challenging inputs. On the other hand, our DGT training strategy dynamically truncates the gradient in the overlap area and the warped garment is no more required to meet the boundary constraint, which effectively avoids the texture squeezing problem.
PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on
In this work, we take a step forwards to explore versatile virtual try-on solutions, which we argue should possess three main properties, namely, they should support unsupervised training, arbitrary garment categories, and controllable garment editing.
Dressing in the Wild by Watching Dance Videos
While significant progress has been made in garment transfer, one of the most applicable directions of human-centric image generation, existing works overlook the in-the-wild imagery, presenting severe garment-person misalignment as well as noticeable degradation in fine texture details.
Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN
Image-based virtual try-on is one of the most promising applications of human-centric image generation due to its tremendous real-world potential.
M3D-VTON: A Monocular-to-3D Virtual Try-On Network
Virtual 3D try-on can provide an intuitive and realistic view for online shopping and has a huge potential commercial value.
WAS-VTON: Warping Architecture Search for Virtual Try-on Network
Despite recent progress on image-based virtual try-on, current methods are constraint by shared warping networks and thus fail to synthesize natural try-on results when faced with clothing categories that require different warping operations.
