MMVP: Motion-Matrix-Based Video Prediction

Yiqi Zhong, Luming Liang, Ilya Zharkov, Ulrich Neumann; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 4273-4283


A central challenge of video prediction lies where the system has to reason the object's future motion from image frames while simultaneously maintaining the consistency of its appearance across frames. This work introduces an end-to-end trainable two-stream video prediction framework, Motion-Matrix-based Video Prediction (MMVP), to tackle this challenge. Unlike previous methods that usually handle motion prediction and appearance maintenance within the same set of modules, MMVP decouples motion and appearance information by constructing appearance-agnostic motion matrices. The motion matrices represent the temporal similarity of each and every pair of feature patches in the input frames, and are the sole input of the motion prediction module in MMVP. This design improves video prediction in both accuracy and efficiency, and reduces the model size. Results of extensive experiments demonstrate that MMVP outperforms state-of-the-art systems on public data sets by non-negligible large margins (approx. 1 db in PSNR, UCF Sports) in significantly smaller model sizes (84% the size or smaller). Please refer to for the official code and the datasets used in this paper.

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@InProceedings{Zhong_2023_ICCV, author = {Zhong, Yiqi and Liang, Luming and Zharkov, Ilya and Neumann, Ulrich}, title = {MMVP: Motion-Matrix-Based Video Prediction}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {4273-4283} }