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1
Intro
2
Overview
3
Frame Interpolation Techniques
4
Sparse Optical Flow
5
Common Frame Interpolation Implementation
6
Bi-directional flow-guided Interpolation
7
Super SloMo: Base Supervised Architecture
8
Limitations on the state-of-the-art
9
Unsupervised Video Interpolation
10
Unsupervised Interpolation
11
Cycle Consistency GAN
12
Cycle Consistency Cost Function
13
Time Domain Consistency Constraint
14
Pseudo Supervised Loss
15
Primary Objective Function
16
Final Training Loss Function
17
Dataset and Metrics
18
Experiment Setup: Datasets Used
19
Experiment Setup: Training
20
Evaluation Methods
21
Experiment: Low Resolution Unsupervised Training
22
Experiment: Domain Gap Testing
23
Experiment: Fine Tune Domain Transfer
24
Experiment: Qualitative Results
25
Ablation: Optimal Weights
26
Conclusion
Description:
Explore advanced techniques in unsupervised video interpolation using cycle consistency in this 30-minute lecture from the University of Central Florida. Delve into frame interpolation methods, including sparse optical flow and bi-directional flow-guided interpolation. Examine the limitations of supervised architectures like Super SloMo and discover how unsupervised approaches overcome these challenges. Learn about cycle consistency GANs, time domain consistency constraints, and pseudo-supervised loss functions. Analyze experimental setups, datasets, and evaluation methods used to test low-resolution training, domain gap testing, and fine-tuning for domain transfer. Gain insights from qualitative results and ablation studies on optimal weights, enhancing your understanding of cutting-edge video interpolation techniques.

Unsupervised Video Interpolation Using Cycle Consistency

University of Central Florida
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