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Computational Photography and Video: Time-Lapse Video Analysis & Editing

Computational Photography and Video: Time-Lapse Video Analysis & Editing. Prof. Marc Pollefeys Dr. Gabriel Brostow. Last Time. Today’s schedule. Factored Time-Lapse Video Sunkavalli, Matusik, Pfister, Rusinkiewicz, Siggraph 2007 Computational Time-Lapse Video

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Computational Photography and Video: Time-Lapse Video Analysis & Editing

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  1. Computational Photography and Video: Time-Lapse VideoAnalysis & Editing Prof. Marc Pollefeys Dr. Gabriel Brostow

  2. Last Time

  3. Today’s schedule • Factored Time-Lapse Video • Sunkavalli, Matusik, Pfister, Rusinkiewicz, Siggraph 2007 • Computational Time-Lapse Video • Bennett & McMillan, Siggraph 2007 • Video Synopsis & Indexing, • Pritch, Rav-Acha, Peleg, ICCV 2007

  4. Time-lapse Photography • Definition: when frames are captured at a lower rate than the rate at which they will ultimately be played back. • Compare to • Slow-motion • Bullet-time

  5. Examples

  6. Time-lapse Filmmaker’s Challenges

  7. Time-lapse Filmmaker’s Challenges • Lighting changes (strobing) • High frequency motion (missed action) • Saturation & exposure • Camera motion: intentional or not • Triggering / data storage / camera safety • Useful? Editable?

  8. Factored Time-Lapse Video Sunkavalli, Matusik, Pfister, Rusinkiewicz SIGGRAPH 2007 Project web page

  9. Remember Intrinsic Images from Video? • Weiss ICCV’01

  10. Outdoors: More Than Just Color vs Illumination? Original No Shadows Given: Daytime, under clear-sky conditions

  11. Outdoors: More Than Just Color vs Illumination? Wishlist Remove shadows Render new shadows Change albedo Change global illumination Original No Shadows Given: Daytime, under clear-sky conditions

  12. Outdoors: More Than Just Color vs Illumination? Original No Shadows Lighting due to sun? sky? Surface Normals?

  13. Formulation • size: width x height x time • F: xyt volume of frames over time • Isky: Accumulated intensity due to sky-light • Isun: Accumulated intensity due to sun-light • Ssun: Binary; is a pixel in shadow

  14. Intuition

  15. Separate Sky first • Photoshop! • Just to pick “non-surfaces” • When is sun’s contribution == 0? • Leaving just sky’s contribution

  16. “Definitely” shadow: median of darkest 20%, then threshold at 1.5x

  17. Bilateral Filter “Definitely” Shadow

  18. How Did Skylight Change? • Isky: Accumulated intensity due to sky-light • Wsky: Sky-light image • Hsky: Sky-light basis curve (1D function!!) • Whole scene got brighter/darker together

  19. Blue: skylight curve (same one)

  20. Factorization • Decompose appearance into • per-pixel W • H curve • Alternating Constrained Least Squares (ACLS): • H(t) is held fixed while W is optimized using least squares, then vice versa

  21. ACLS: Alternating Constrained Least Squares • Inverse shade trees for non-parametric material representation and editing, • Lawrence et al., Siggraph 2006, code online

  22. Original and Isky

  23. Isky and Reconstruction

  24. Explained Sky…

  25. Now Decompose Sun’s Contributions • Isun: Accumulated intensity due to sun-light • Wsun: Sun-light image, per-pixel weights • Hsun: Sun-light basis curve • : Shift-map; per pixel offset in time

  26. Isun: Accumulated intensity due to sun-light • Wsun: Sun-light image, per-pixel weights • Hsun: Sun-light basis curve • : Shift-map; per pixel offset in time

  27. Isun: Accumulated intensity due to sun-light • Wsun: Sun-light image, per-pixel weights • Hsun: Sun-light basis curve • : Shift-map; per pixel offset in time

  28. Total sunlight contribution Sunlight image (like on moon) Shift map Sunlight

  29. Factorize Again! • For each image, subtract off Isky(t) • Ssun: Sky-light mask, serves as C, confidence map • ACLS again • But add 3rd update phase, searching for that minimizes reconstruction error of scaled + offset H(t) vs. F(t)

  30. Green: sunlight curve (same one, but time-shifted)

  31. Isun(t) Wsun

  32. Reconstruction

  33. Does It Work?

  34. Video: FactoredTimeLapseV

  35. What have we gained? • Can • Remove shadows • Change albedo • Change amount of global illumination • Compression • Render new shadows: • We have 1 component of the per-pixel Normals, N

  36. Estimated Normals (1D)

  37. Recap: Manipulating Normals

  38. Does the old definition of time-lapse still hold?

  39. Today’s schedule • Factored Time-Lapse Video • Sunkavalli, Matusik, Pfister, Rusinkiewicz, Siggraph 2007 • Computational Time-Lapse Video • Bennett & McMillan, Siggraph 2007 • Video Synopsis & Indexing, • Pritch, Rav-Acha, Peleg, ICCV 2007 Still 1 frame at a time Examine parts of frames

  40. (Quick Overview of)Computational Time-Lapse Video Bennett & McMillan Siggraph 2007 (Project web page)

  41. Sampling in 1D • Uniform • Interpolate linearly+ piece-wise • Same # of samples, but where needed! • Optimum polygonal approximation of digitized curves, Perez & Vidal, PRL’94 • Use dynamic programming to find global min.

  42. Completely User-controllable:Min-Change, Min-Error, Median, etc

  43. Video

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