Asymmetric Bilateral Motion Estimation for Video Frame Interpolation
About
We propose a novel video frame interpolation algorithm based on asymmetric bilateral motion estimation (ABME), which synthesizes an intermediate frame between two input frames. First, we predict symmetric bilateral motion fields to interpolate an anchor frame. Second, we estimate asymmetric bilateral motions fields from the anchor frame to the input frames. Third, we use the asymmetric fields to warp the input frames backward and reconstruct the intermediate frame. Last, to refine the intermediate frame, we develop a new synthesis network that generates a set of dynamic filters and a residual frame using local and global information. Experimental results show that the proposed algorithm achieves excellent performance on various datasets. The source codes and pretrained models are available at https://github.com/JunHeum/ABME.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Video Frame Interpolation | Vimeo90K (test) | PSNR36.18 | 131 | |
| Video Frame Interpolation | UCF101 | PSNR35.38 | 117 | |
| Video Interpolation | UCF-101 (test) | PSNR35.38 | 65 | |
| Video Frame Interpolation | Vimeo90K | PSNR36.18 | 62 | |
| Video Frame Interpolation | SNU-FILM Extreme | PSNR25.42 | 59 | |
| Video Frame Interpolation | SNU-FILM Hard | PSNR30.58 | 59 | |
| Video Frame Interpolation | SNU-FILM Medium | PSNR35.77 | 59 | |
| Video Frame Interpolation | SNU-FILM Easy | PSNR39.59 | 59 | |
| Multi-frame Video Interpolation | X 4K (test) | PSNR30.16 | 43 | |
| Video Frame Interpolation | Middlebury | Average IE Error2.01 | 42 |