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Enterprise-Grade AI Virtual Models for High-Fidelity Garment Visualization
High-Resolution Virtual Try-On with Misalignment and Occlusion Handling

HR-VITON is an open-source PyTorch implementation for high-resolution virtual try-on, addressing misalignment and occlusion issues. It proposes a novel try-on condition generator as a unified module for warping and segmentation generation. A feature fusion block facilitates information exchange, avoiding misalignment and pixel-squeezing artifacts. Discriminator rejection filters incorrect segmentation map predictions. The model is trained and evaluated using the VITON-HD dataset, demonstrating superior performance in handling misalignment and occlusion compared to baselines. Its architecture facilitates synthesizing realistic images of individuals wearing different clothing items, solving issues in traditional methods that separate warping and segmentation stages.
HR-VITON is an open-source PyTorch implementation for high-resolution virtual try-on, addressing misalignment and occlusion issues.
Explore all tools that specialize in clothing warping. This domain focus ensures HR-VITON delivers optimized results for this specific requirement.
Explore all tools that specialize in virtual try-on. This domain focus ensures HR-VITON delivers optimized results for this specific requirement.
Explore all tools that specialize in image synthesis. This domain focus ensures HR-VITON delivers optimized results for this specific requirement.
Explore all tools that specialize in segmentation generation. This domain focus ensures HR-VITON delivers optimized results for this specific requirement.
A unified module for clothing warping and segmentation generation stages, preventing misalignment and pixel-squeezing artifacts.
Implements information exchange within the condition generator to prevent misalignment.
Filters out incorrect segmentation map predictions, ensuring the performance of virtual try-on frameworks.
Designed to work with high-resolution datasets, allowing for detailed and realistic virtual try-on results.
Specifically addresses misalignment and occlusion challenges in virtual try-on scenarios.
Clone the repository: git clone https://github.com/sangyun884/HR-VITON.git
Navigate to the HR-VITON directory: cd ./HR-VITON/
Create a conda environment: conda create -n {env_name} python=3.8
Activate the environment: conda activate {env_name}
Install PyTorch and dependencies: conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia
Install additional dependencies: pip install opencv-python torchgeometry Pillow tqdm tensorboardX scikit-image scipy
Download the VITON-HD dataset and place it in ./data
All Set
Ready to go
Verified feedback from other users.
"HR-VITON offers high-quality virtual try-on with realistic results, especially in handling misalignments and occlusions, but the setup can be complex for non-technical users."
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