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X - frame
An attempt on frame interpolation with GAN
\infin
∞
\infin
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What is frame interpolation?
A brief review on frames
Image from Lecture slides
\alpha
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\alpha
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1-\alpha
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1-\alpha
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Generate in-between frames
Synthetic video dataset
We have this claw and ball...
Sample Frames
\alpha = 0.7
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0.7
\alpha = 0.7
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0.7
\alpha = 0.6
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0.6
\alpha = 0.6
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0.6
\alpha = 0.2
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0.2
\alpha = 0.2
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0.2
\alpha = 0.9
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0.9
\alpha = 0.9
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WGAN-GP with U-Net
WGAN-GP with U-Net
Results
Start_Frame GT Predicted End_Frame
Forced learning?
Generator Pyramid Loss
...
s
real
...
s
fake
Discriminator
py_gen
Results
Start_Frame GT Predicted End_Frame
Discriminator Pyramid Loss
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B-frame
...
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A-frame
Discriminator
α
1-α
...
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M-frame
py_dis
Results
Start_Frame GT Predicted End_Frame
The real stuff
movie <Zathura>
Sample Frames
\alpha = 0.94
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\alpha = 0.94
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0.94
\alpha = 0.40
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0.40
\alpha = 0.40
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0.40
\alpha = 0.55
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0.55
\alpha = 0.55
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WGAN-GP with U-Net
WGAN-GP with U-Net
Results
Start_Frame GT Predicted End_Frame
Conclusion
Forced learning may be helpful at times
Phase interpolation is learnable by a simple GAN
Balanced WGAN loss + L2 loss
Resume presentation
X - frame An attempt on frame interpolation with GAN \infin ∞
Inf-Frames
By Arvin Liu
Source:
james01
Inf-Frames
6 years ago
1,078
Arvin Liu
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