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Structured Face Hallucination

Structured Face Hallucination. Chih-Yuan Yang Sifei Liu Ming-Hsuan Yang Electrical Engineering and Computer Science. Outline. Motivation Related work Proposed method Experimental results Conclusions. Motivation. Algorithm. Generate high-quality face images. Challenges.

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Structured Face Hallucination

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  1. Structured Face Hallucination Chih-Yuan Yang Sifei Liu Ming-Hsuan Yang Electrical Engineering and Computer Science

  2. Outline • Motivation • Related work • Proposed method • Experimental results • Conclusions

  3. Motivation Algorithm Generate high-quality face images

  4. Challenges • How to effectively model a face? • Landmark points • How to preserve the consistency of details? • Transfer details of a whole component • Maintain consistency of edges in upsampling • Exploit statistics of edge sharpness

  5. Face Hallucination [Liu07] • PCA on intensities • Global constraint • MRF on residues • High-frequency details • Bilateral filtering as post-processing • Suppress ghost effects

  6. Sparse Representation [Yang08] • NMF on intensity • Global constraint • Patch mapping through a pair of sparse dictionaries • Restore the high-frequency details

  7. Position Patch [Ma10] • No global constraint • Only local constraint by patch position • Only use exemplar patches at the same position • Weighted averaging exemplar patches

  8. Proposed Approach • Three classes • facial components • Transfer the HR details from the whole region of a component • edges • Preserve edge structures and restore sharpness by statistical prior • smooth regions • Transfer the HR details from small patches

  9. Aligning Component Exemplars Align high-resolution exemplar images : coordinates of landmark points Generate low-resolution exemplar images Search for the most similar exemplar • Exemplar images are labeled • Each component is aligned individually

  10. Insights • Consistency • Consistent details because the whole component is transferred • The pair of eyes is considered as one component, as well as the eyebrows • Effectiveness • Landmark points enable the comparison for a whole component • Effective for various shapes, sizes, and positions

  11. Preserve Edge Structures Directional similarity in LR patches Bilinear interpolation preserves the directional similarity in HR Regularize the HR image Direction-Preserving Upsampling

  12. Restore Edge Sharpness upsampled edge center mag. of grad. enlarged restored restored Statistical priors

  13. Smooth Regions • Approach • Find the most similar LR patch and transfer the HR gradients • Advantage • Highly adaptive • Achieved by • PatchMatchalgorithm • Low computational load • Restriction • Consistency • Accuracy Component Exemplar Patch only Edge Model and Priors Patch only

  14. Generate Output Images Merge gradient maps Generate output images

  15. Experimental Results

  16. Conclusions • Structured face hallucination • Effective whole component exemplars • Preserved edge structures and robust statistical sharpness priors • Preliminary results • Effective and consistent high-frequency details • Robustness

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