Convolutional Neural Networks (Part III) 08, 10 & 17 Nov, 2016 J. Ezequiel Soto S. Image Processing 2016 Prof. Luiz Velho Convolutional Neural Networks 1
Summary & References 08/11 ImageNet Classification with Deep Convolutional Neural Networks 2012, Krizhevsky et.al. [source] 10/11 Going Deeper with Convolutions 2015, Szegedy et.al. [source] 17/11 Painting Style Transfer for Head Portraits using Convolutional Neural Networks 2016, Selim & Elgharib [source] + A Neural Algorithm of Artistic Style 2015, Gatys et.al. [source] + Image Style Transfer Using Convolutional Neural Networks 2016, Gatys et.al. [source] Convolutional Neural Networks 2
Painting Style Transfer for Head Portraits using Convolutional Neural Networks Selim & Elgharib, 2016 Convolutional Neural Networks 3
Outline ● Motivation ● Related work – Painting Transfer – Color Transfer – Extension to Video ● Painting trough CNNs ● Example Driven Spatial Constrains – Modified feature maps – Solving for the painting ● Results – Still images – Extension to video ● Discussion Convolutional Neural Networks 4
Motivation ● Usual style-transfer techniques often deform facial structures: no spatial constrains. → Human perception is very sensitive to changes in faces. ● Other strategies: – facial geometry (limited artistic styles: graphite & sketch painting) – image analogies (requires paired images: limited applicability) – color transfer: power maps (structure and color / no texture) Gatys, photo et.al. Selim, example et.al. Convolutional Neural Networks 5
Related: Painting Transfer ● Stroke based Example: Gatys et.al. – Simulates brush-stroke → Facial deformation!!! placement process – Facial landmarks as – Brush attributes: scale, guidance (AAM / ASM): orientation, color, opacity eyes, eyebrows, nose, ● Texture transfer mouth, face outline – Follow sample textures – Parts based techniques: – Texture synthesis non parametric sampling, ● Parts transfer MRF and spatial smoothness in – Parse the input image in parts reconstruction: graphite – Reconstruction from parts & sketch database Convolutional Neural Networks 6
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Related Related: Color Transfer Related: Video ● Transfer color palette from ● Applying painting frame by example to input frame generates: flickering & shower-door. ● Not based in image ● Flickering: temporal analogies inconsistencies. ● Power maps: local statistics. → ● Shower-door: texture drifts Local distribution of light away from the object. ● Spatial constraints: warping ● Search better style, transfer (facial landmarks as reference) style to frame 1, propagate to others by optical flow ● Does not capture texture. and transfer after that. Convolutional Neural Networks 8
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Gatys, et.al. approach Convolutional Neural Networks 10
Gatys, et.al. results Convolutional Neural Networks 11
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The improved model ● Modified loss function – All levels of detail to maintain structure (reduce deformation) – Gain maps: local statistics → between input and example alignment!!! ● Alignment: – Find landmarks (66) → align: ● morphing (eyes / mouth) ● sift-flow (facial contour) E: example I: input M: modified w/ Gain Map O: output Convolutional Neural Networks 14
Γ = 1, 10, 100 Convolutional Neural Networks 15
Gain maps only Convolutional Neural Networks 16
Results ● Gain map clamp: [0.75, 5] ● Activation of the parameters for the 3th and 4 th convolutional layer only ● Style / identity: ( Γ = 100) ● Comparison against original Gatys, et.al. And a modified version 1) Preserves input identity 2) Transfer local spatial color distribution of the style 3) Reduce deformation during texture transfer Convolutional Neural Networks 17
Clamping + Stages Convolutional Neural Networks 18
Ghosting Convolutional Neural Networks 19
Alignment Convolutional Neural Networks 20
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Video [video] Convolutional Neural Networks 25
Discussion ● CNN + spatial constrains: improved results ● Needs only one example and works with any style ● Experimented with a wide range of portraits and styles ● Temporal coherent results for video ● Very specific application with limitations ● CNN are powerful, but it could (should) be combined with robust classic strategies for better results ● Each application needs to adapt the “trendy” tech and use specific knowledge base Convolutional Neural Networks 26
The end Convolutional Neural Networks 27
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