Methods › Computer Vision › Video Interpolation Models › FLAVR
FLAVR
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
FLAVR is an architecture for video frame interpolation. It uses 3D space-time convolutions to enable end-to-end learning and inference for video frame interpolation. Overall, it consists of a U-Net style architecture with 3D space-time convolutions and deconvolutions (yellow blocks). Channel gating is used after all (de-)convolution layers (blue blocks). The final prediction layer (the purple block) is implemented as a convolution layer to project the 3D feature maps into (k−1) frame predictions. This design allows FLAVR to predict multiple frames in one inference forward pass.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Video Frame Interpolation for Polarization via Swin-Transformer 17 Jun 2024 · 0 repositories · arXiv:2406.11371
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FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation 15 Dec 2020 · 1 repository · arXiv:2012.08512
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Video Frame Interpolation | 2 |
| Action Recognition | 1 |
| Motion Magnification | 1 |
| Optical Flow Estimation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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