Computer Vision in Graphics Production

 
Where is computer vision useful in content production? Where is vision ued? What can and can’t current computer vision do?
By: Lokesh
1 years 2 months ago
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Presentation Transcript
Slide1 : Computer Vision in Graphics Production Visual Media Research Group Centre for Vision, Speech and Signal Processing University of Surrey
Slide2 : Overview Where is computer vision useful in content production? Where is vision used? What can and can’t current computer vision do?
Slide3 : Computer Vision in Content Production Computer vision: analysis & interpretation of real images/video Match moving Matting Model building Human motion capture Studio Production Facial animation Image-based illumination
Slide4 : Match Moving Reconstruction of camera movement to composite CG Products: 2d3, RealViz … (semi-automatic camera tracking) … standard tool in film production Oxford University/2d3
Slide5 : Matting Separation of foreground and background objects - actor/background separation - wire/set removal Studio: chroma-key (solved) Post-production: Imagineer, RealViz, …. (open-problem)
Slide6 : Model Building Capture of real shape for CG models objects environments characters Shape capture active sensors (laser/structured-light) - accurate 3D surface measurements - static objects or environments structure from images - low-accuracy - static objects Problem: unstructured surface measurements
Slide7 : Model Building (2) Walking with Dinosaurs - FrameStore
Slide8 : Structured Models
Slide9 : Courtesy Stanford Computer Graphics Lab. Animation of David
Slide10 : Human Motion Capture Capture of real motion Marker based systems widely used in performance animation whole-body/face character animation (Golum) ‘realistic’ motion characteristics accurate real-time? widely used in film production (with post-production)
Slide11 : Human Motion Capture(2) Markerless human motion capture advantages: unintrusive; simultaneous appearance capture model-based visual tracking low-accuracy visual ambiguity (uniform apperance, non-rigid shape)
Slide12 : Human Motion Capture(3)
Slide13 : Studio Production Modelling actors 3D animated models animation re-illumination loss of visual realism to captured images Free-viewpoint video post-production of arbitrary camera views/paths ‘matrix’ flowmo shots aim: quality equivalent to captured video
Slide14 : Studio Production (2)
Slide16 : Limitations: - shape detail face/hands/hair - not video quality Studio Production (3)
Slide17 : Studio Production: Free-view video
Slide18 : Studio Production: Free-view video
Slide19 : Studio Production: Free-view video
Slide20 : Studio Production: Free-view video
Slide21 : Facial Animation Production of photo-realistic faces 2D video-based approaches video rewrite [Bregler’97] concatenative synthesis [Cossato’98,Ezzat’02] photo-realistic limited viewpoint, illumination 3D markers Performance animation [Phigin’98] shape only 3D video (shape+appearance) concatenative synthesis photo-realistic control of viewpoint, illumination
Slide22 : 3D Video
Slide23 : 3D Video Database 51 people: Expressions + Speech (short sentence)
Slide24 : Initial Face Synthesis from Speech Input: Output:
Slide25 : Image-based illumination Illumination of CG scenes with real illumination high-dynamic range capture of illumination environment mapping illumination to CG scene widely used in production Debevec SIGGRAPH’00 FiatLux
Slide26 : Computer Vision in Content Production Match moving - yes, automatic camera tracking available Matting - partial, many unsolved problems Model capture – yes, tools for semi-automatic restructuring Human motion capture – no, inaccurate Studio Production – partial, free viewpoint video Facial animation – yes, 3D video Image-based illumination – yes, widely used Other applications of computer vision….
Slide27 : Future Directions Video-based rendering photo-realistic, free-viewpoint rendering of dynamic scenes relighting required Video-based animation animation from captured video (face,whole-body…) control of motion, viewpoint, illumination
Slide28 : People Jon Starck, Joel Mitchelson, Gordon Collins, Eng-Jon Ong, Ioannis Ypsilos, Rob Dilks, Michael Kalkavouras Collaborators BBC, BT, Sony, Canon, Philips, Hensons, Framestore, Snell&Wilcox, 3D Scanners, AvatarMe EPSRC – DTI