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Skeleton/Gesture Recognition Two key observations: (1) There are systematic errors which enable us to learn alignment function for the correction. (2) The alignments are nonlinear and non-deterministic.
Skeleton/Gesture Alignment •The skeletons are aligned w/ numerical values •The numerical values are used to drive the avatar
Page 1 and 2: Microsoft Research Asia Update Hsia
Page 3 and 4: Microsoft Research Research lab loc
Page 5 and 6: People and Research Areas Hsiao-Wue
Page 7 and 8: Research Groups Hardware Computing
Page 9 and 10: Research Updates Natural User Inter
Page 11 and 12: Kinect based Object Digitization Si
Page 13 and 14: Comparisons 35,862 Triangles 36MB M
Page 15 and 16: Avatar Kinect Facial expression tra
Page 17: Xbox Kinect Pipeline: Skeleton Corr
Page 21 and 22: Output Distribution Model Random fo
Page 23 and 24: Qualitative Results
Page 25 and 26: Kinect Based 3D Face Avatar 3D face
Page 27 and 28: Kinect Based 3D Face Avatar (Fang Y
Page 29 and 30: Goal: 3D Hair from Single Image Sin
Page 31 and 32: Result Single portrait image + user
Page 33 and 34: Strand Tracing
Page 35 and 36: 3D Strands: hair volume Additional
Page 37 and 38: Portrait Pop-ups: layers completion
Page 39 and 40: Hairstyle Replacement Build pop-up
Page 41 and 42: Hairstyle Replacement: more results
Page 43 and 44: Limitations Not a complete 3D model
Page 45 and 46: 3D Hair Replacement
Page 47 and 48: Research Updates Search and online
Page 49 and 50: What Are Attributes? Clipart Linedr
Page 51 and 52: What Are Attributes? Simple Backgro
Page 53 and 54: Color/Clipart/Linedrawing Filter
Page 55 and 56: Color Filter Examples dog + red Red
Page 57 and 58: Color Filter Examples sunset + blue
Page 59 and 60: Intention Analysis (w/o Query Objec
Page 61 and 62: Original Images
Page 63 and 64: Our Solution • Salient object det
Page 65 and 66: Our Solution • Salient object det
Page 67 and 68: BMW + blue
Page 69 and 70:
BMW+ red
Page 71 and 72:
dog
Page 73 and 74:
dog + blue
Page 75 and 76:
dog + black
Page 77 and 78:
forest
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forest + red
Page 81 and 82:
sunset
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sunset + green
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tulips
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tulips + purple
Page 89 and 90:
Taylor Swift
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Taylor Swift + red
Page 93 and 94:
Important Filters Usage on image se
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Cascaded Filtering Simple Backgroun
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Cascaded Filtering Web Images Backg
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Cascaded Filtering Shape Filter: Pr
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Cascaded Filtering Texture Filter:
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Page 105 and 106:
Cascaded Filtering Photo Filter: Cl
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Cascaded Filtering Text Filter: Tex
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Page 111 and 112:
Relevance ≠ Clipart Relevance Ran
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Results without Clipart Specific Te
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Results without Clipart Specific Te
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Bing Clipart Filter • Visual filt
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Results
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Cascaded Filtering Text Filter: Fil
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Motivation Visual quality is import
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Integrate Visual Features Color- Hi
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Image Quality Related Attributes Pe
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Image Quality Related Attributes Ob
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Image Quality Related Attributes Im
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Image Quality Related Attributes Ge
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Comparison Results Old results (que
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Comparison Results Old results (que
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Research Updates Data Intensive Com
Page 144 and 145:
Urban Computing Improving Sensing U
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1. GPS trajectories of 33,000 taxis
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3. Ticketing data of public transpo
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5. Real estate market
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7. POI Data (2007 - 2012)
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Discover Regions of Different Funct
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Applications • Calibrating urban
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Methodology Overview Mapping from r
Page 160 and 161:
Annotation of Territories Emerging
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Research Updates CS Fundamentals Th
Page 164 and 165:
GPUs vs. Programmers GPU architectu
Page 166 and 167:
Research on BSGP (2007~now) Compile
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Contribution to Community BSGP down
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Motivation Growing demand for fineg
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Key Problem How to efficiently buil
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Enabling Walking-based Collection I
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Path/Trace list # of Effective turn
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End-to-end Evaluation* 100% 95% 90%
Page 180 and 181:
An Illustrative Example Start Actua
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Conclusion MSRA follows the same mi
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