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sRender

We provide Pytorch implementation for sRender: rendering facial sketches

Citation:

Meimei Shang, Fei Gao *, Xiang Li, Jingjie Zhu, Lingna Dai. Bridging Unpaired Facial Photos And Sketches By Line-drawings. 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6-11 June 2021, Toronto, Ontario, Canada. (Accepted)

[paper@arXiv] [project@github]

Pipeline

Reconstructed sketches

Sketch Reconstruction

left: croquis style; right: charcoal style Reconstructed sketches (a)Sketches ; (b)Generated Line-drawings ; (c)Synthesized Sketches

Oil Painting Reconstruction

Reconstructed oil paintings (a)Oilpaintings ; (b)Generated Line-drawings ; (c)Synthesized Oil Paintings

Sketch Generation (from photos)

left: croquis style; right: charcoal style Generated sketches (a)Photos ; (b)Generated Line-drawings ; (c)Synthesized Sketches

Oil Painting Generation (from photos)

Generated oilpaintings (a)Photos ; (b)Generated Line-drawings ; (c)Synthesized Oil Paintings

Comparison

  • Croquis sketches generated by our sRender and unpaired I2I translation methods

compare with SOTA

Train and test

  • charcoal_style for systhesising charcoal style images,it contains sRender w/o Lstr for model without stroke_loss and sRender for model with stroke_loss correspodingly
  • croquis_style for systhesising croquis style images,it contains sRenderPix2Pix, sRender w/o Lstr and sRender
  • download dataset gray for charcol_style, binary for croquis_style
  • download pretrain model and put them in ./checkpoints for test
  • for model with stroke_loss, you should download stroke_model and specify model root in ./models/pix2pixHD_model for net_c.load_state_dict
  • you can modify options/base_option to specify --dataroot, then run train.py or test.py

Dataset

Pretrained models

Results

  • Our synthesis result for croquis and charcoal style can be downloaded from Goole Drive
  • Our synthesis result for oil painting style can be downloaded from Goole Drive

Acknowledgments

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Facial Sketch Render

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  • Python 98.8%
  • Shell 1.2%