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Deep Photo Style Transfer is an implementation of the algorithm described in the paper “Deep Photo Style Transfer” (arXiv 1703.07511). The software allows users to transfer the style of one photograph to another while preserving photorealism and semantic consistency. It relies on semantic segmentation masks to guide style transfer (so that e.g. sky maps to sky, building maps to building), and uses a matting Laplacian regularization term to ensure smooth transitions. The repository provides code in Torch (Lua), MATLAB / Octave scripts for computing the Laplacian, and pre-trained models. Pretrained models and example scripts for ease of use. Compatibility with MATLAB / Octave for Laplacian computations.
Features
- Semantic-aware style transfer (style guided by segmentation masks)
- Photorealistic regularization via matting Laplacian
- Multi-stage pipeline: intermediate segmentation + final transfer
- Support for CUDA / GPU acceleration
- Compatibility with MATLAB / Octave for Laplacian computations
- Pretrained models and example scripts for ease of use
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Categories
Machine LearningFollow Deep Photo Style Transfer
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