1. VMorph: Motion and Feature-Based Video Metamorphosis
Robert Szewczyk
Andras Ferencz
Henry Andrews

2. Automatic Video Morphing
Currently, to do a video morph, a user must:
manually enter feature lines in a number of key frames
linearly interpolate to find the feature lines in the remaining frames
apply still morphing
In order to reduce the amount of user input and time, and improve morphing quality, our algorithm:
Aggregates the feature lines into groups
Tracks the groups of feature lines
Morphs foreground and background separately

3. Groups of Feature Lines
Ensure that lines within the group stay in the same relation to one another
Motion of a group of feature lines is described by an affine transform
No loss of flexibility: animator groups the lines, grouping ranges from a single line per group to all feature lines in a single group

4. Feature Line Group Tracking
Compute the dense motion field MF using census transform
need a dense motion field, since feature lines do not correspond to any edges
Translate all the points in a feature line group using MF
allow for disappearance of any feature line
Fit an affine transform T to the translated points
currently: General Least Mean Squares, with a bit of robustness (discard a percentage of largest outliers), future: General Least Median Squares
Apply T to the feature line group to find the new location of each line in the group

5. Image Segmentation
Observation: foreground and background are distinct and move differently
When morphed simultaneously, different parts of the image influence each other.
Conclusion: they should be morphed separately
still morphing is not aware of image segmentation
for video sequences, the segmentation can be done automatically
Background: use salient stills, reduce the problem to still image morphing
Foreground: morphing with a mask

6. Morphing With a Mask
Create Mask
Add Still
Morph mask
Mask Still

7. Background Reconstruction

8. Feature Line Tracking Results

10. Morphing results: VMorph vs. Still Image Morphing