ImageBased Motion Blur for Stop Motion Animation - PowerPoint PPT Presentation

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ImageBased Motion Blur for Stop Motion Animation

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Each continuous region of pixels within Im is considered to be a 'blob' Pixel Transformations: ... Blob's appearance not greatly affected by scale ... – PowerPoint PPT presentation

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Title: ImageBased Motion Blur for Stop Motion Animation


1
Image-Based Motion Blur for Stop Motion Animation
  • Gabriel J. Brostow
  • Irfan Essa
  • SIGGRAPH 01

2
First Stop Motion Animation
  • Create a Physical Scene
  • Insert Characters / Objects
  • Change Scene Slightly
  • Record Frame and Repeat

3
Why do we want Motion Blur?
  • For fast moving objects
  • Prevent time aliasing
  • Need for a tool to convey fast motions
  • Realistic Perception
  • Notice the absence

4
Overview
  • Segmentation
  • Rigid blob tracking
  • Flow Correction
  • Rendering Blur
  • Results

5
Scene Segmentation
  • Grouping the pixels to be tracked
  • Ib background image
  • If image of current frame
  • Im image of moving pixels, where
  • Each continuous region of pixels within Im
    is considered to be a blob

6
Pixel Transformations
  • Determining frame-by-frame pixel mapping is still
    an on-going research problem
  • They use a two-pass approach

One Commercial Method
Their Method
7
Blob Tracking
  • Perform similarity search between each blob b in
    Im(i) and Im(i1)
  • Reduce search space through assumptions
  • Blobs appearance not greatly affected by scale
  • Rotation of blob can be determined by absolute
    orientation

8
Blob Tracking (cont.)
  • For each b(i), template-match against Im(i1)
    through the normalized cross correlation NCC,
    where
  • Interpolate a vector mapping for pixels from
    frame i to i1

9
Flow Correction
  • Have an estimate for 2-D transformations by the
    scenes blobs
  • Ir(i1) estimate of If(i1) generated by
    applying If(I)s computed vectors
  • Ideal 2-D Ir(i1) If(i1), but other effects
  • Optical flow finds motion vectors to warp Ir(i1)
    to If(i1)

10
Rendering Blur
  • Linearly interpolate pixel paths (L(t))
  • User defines shutter speed (S)
  • Determine time spent in each cell (w(x,y))
  • Distribute pixel (C)

11
Rendering Blur (cont.)
  • IAft(ti) Motion of If(ti) from ti to ti S/2
  • Get IBef(ti) ti to ti - S/2
  • Pixel-wise average these occupancy maps
  • Some pixels have few or no pixels move through
    them

12
Results
  • Examples
  • Representative input and output pair

13
Results
  • Examples
  • Affects of different shutter speeds

14
Results
  • Examples
  • Shutter speed vs. interpolation

15
Results
  • Examples
  • Problems with shadows

16
Fin
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