Style Transfer for Headshot PortraitsYiChang ShihSylvain ParisConnelly BarnesMIT CSAILAdobeUniversity of VirginiaWilliam T. FreemanFrédo DurandMIT CSAILMIT CSAIL
Professional portraits look betterOrdinary photoProfessional photo
The goal: make good portraits easy Makelook likeOrdinary photoProfessional photo Transfer the style from the example photo Automatic
We work on headshots What we match: retouching, texture, lighting What we do not match: pose, expression,clothing, focal length, aperture
Preview our resultInputExampleOutput
Hard problem: color transfer is not sufficient Humans are intolerant to artifacts on facesInputExampleOur method[HaCohen et al. 2010](lighting and detailsare missing)
Related work: global transfer[Bae et al. 2006, Sunkavalli et al. 2010 ] Work well on landscapesInputModel Do not work as well on portraitsOutput by Bae et al. [2006]
Related work: global transfer[Bae et al. 2006, Sunkavalli et al. 2010 ] Work well on landscapesInputModel Do not work as well on portraitsOutput by Bae et al. [2006]
Related work: local style transfer Time hallucination [Shih et al. 2013, Laffont et al. 2014]Input: afternoonExample images Requires two images: before and afterOutput: night
Related work: face enhancement[Joshi et al. 2010, Shih et al. 2013 ] Image restoration: deblurring, denoising Blurred input faceExamplesOutput: deblurred face We focus on photographic stylization.
Problem statement Input: a casual frontal portrait and an example Output:‐ The input portrait rendered in the example style‐ Automatic‐ The style includes texture, tone, and color
Key idea #1: local transfer Local: eyes, nose, skin, etc. are treated differentlyInputExample
Key idea #1: local transfer Local: eyes, nose, skin, etc. are treated differentlyInputExample
Key idea #2: multi‐scale transfer Textures at different scales are treated differentlyPortrait #1Portrait #2
Key idea #2: multi‐scale transfer Textures at different scales are treated differentlyPortrait #1Portrait #2
Overview of the algorithm1. Dense matching between the input and example2. Multiscale transfer of local statistics3. Post processing on eyes and backgroundInputExampleStep 1: matchingStep 2: transfer Step 3: post processing
Step 1: dense matching Rigid warp SIFT flow to align semantic features[Liu et al. 2008]InputExampleWarped example
Step 2: multi‐scale local transferInputExample
Step 2: multi‐scale local transfer1. Construct Laplacian stacks for the input and the exampleInputExample
Step 2: multi‐scale local transfer1. Construct Laplacian stacks for the input and the exampleInputExample2. Local matchat each scale
Step 2: multiscale transfer of local statistics1. Construct Laplacian stacks for the input and the exampleInput2. Local matchat each scaleExample3. Collapse the matched stacks to create the output of this stepOutput
Step 2: multi‐scale local transfer1. Construct Laplacian stacks for the input and the exampleInput2. Local matchat each scaleExample3. Collapse the matched stacks to create the output of this stepOutput
Local energyExample LaplacianLocal energyℓGaussian kernel at this scale
At each scale: match local energyInput energyExample energy
At each scale: match local energyComputethe gain mapExample LaplacianLocal energy S[E]Gain map Input LaplacianLocal energy S[I]
At each scale: match local energyComputethe gain mapLocal energy S[E]Example LaplacianGain map Input LaplacianModulatethe input LaplacianLocal energy S[I] Input LaplacianGain mapOutput Laplacian
Robust transfer Clamp the gain map to avoid artifactscaused by moles or glasses on the exampleInputExampleWithout robust transferOur robust transfer
Laplacian using a face mask Preserve the hair boundary using normalizedconvolution and a face maskInputExampleWithout using the mask(the edges disappear)Our method(the edges are preserved)
Step 3: post‐processing Adding eye highlights Replacing the backgroundInputExampleWithout eye highlights Adding eye highlights(Our final result)
Algorithm recapInputExampleStep 1.Dense alignment
Algorithm recapInputExampleStep 2.Step 1.Dense alignment Local transfer
Algorithm recapInputExampleStep 2.Step 1.Dense alignment Local transferStep 3.Eyes andbackground
Automatic example selection Retrieve the best examples based on the facesimilarity between the inputInputThe top three retrieved results
Automatic example selection The results are robust to the example choicesInputStyle transferred results using the top three examples
ResultsInputExamples are shown in the insetsStyle 1Style 2Style 3
Close‐upInputExampleOutput
ExampleOutput
More resultsInputStyle 1Style 2Style 3
Outdoor inputInputStyle 1Style 2Style 3
Extra resultsInputStyle 1Style 2Style 3
ComparisonsInputExampleGlobal transfer[Bae et al. 2006]Our result
InputExampleHistogram transfer [Reinhard et al. 2001]Our method[Pitié et al. 2007][Sunkavalli et al. 2010]Photoshop Match Color
Different success levels: good results The inputs are well litInputOutput
Hard case Matting (face mask) failureInputOutput
Limitations Require the input and the example to have similar facialattributes, e.g., skin color Cannot handle hard shadows on the inputInputExampleFailure output
Evaluation 94 headshot inputsfrom Flickr Available on ourwebsite
Extension to videos
Conclusion We introduce a style transfer algorithm tailored forheadshot portraits. Based on multiscale transfer of local image statisticsInputExampleOutput
Code and data are available Matlab code Flickr evaluation datasetpeople.csail.mit.edu/yichangshih/portrait web/
Acknowledgments We thank Kelly Castro for discussing with ushow he works and for his feedback, MichaelGharbi and Krzysztof Templin for being ourportrait models. We acknowledge the funding from QuantaComputer and Adobe.
Conclusion We introduce a style transfer algorithm tailored forheadshot portraits. Based on multiscale transfer of local image statisticsInputExampleOutput
Ordinary photo Professional photo. We work on headshots What we match: retouching, texture, lighting What we do not match: pose, expression, clothing, focal length, aperture . Input: a casual frontal portrait and an example
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