Last updated on: 10:00 May 13, 2024

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Note: # = Equal Contribution , * = Corresponding Author.

2024

CVPR24 ExtDM: Distribution extrapolation diffusion model for video prediction
Zhicheng Zhang#, Junyao Hu#, Wentao Cheng*, Danda Paudel, Jufeng Yang

TL;DR: We present ExtDM, a new diffusion model that extrapolates video content from current frames by accurately modeling distribution shifts towards future frames.

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Details
Abstract: Video prediction is a challenging task due to its nature of uncertainty, especially for forecasting a long period. To model the temporal dynamics, advanced methods benefit from the recent success of diffusion models, and repeatedly refine the predicted future frames with 3D spatiotemporal U-Net. However, there exists a gap between the present and future and the repeated usage of U-Net brings a heavy computation burden. To address this, we propose a diffusion-based video prediction method that predicts future frames by extrapolating the present distribution of features, namely ExtDM. Specifically, our method consists of three components: (i) a motion autoencoder conducts a bijection transformation between video frames and motion cues; (ii) a layered distribution adaptor module extrapolates the present features in the guidance of Gaussian distribution; (iii) a 3D U-Net architecture specialized for jointly fusing guidance and features among the temporal dimension by spatiotemporal-window attention. Extensive experiments on five popular benchmarks covering short- and long-term video prediction verify the effectiveness of ExtDM.

BibTex
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@inproceedings{zhang2024ExtDM,
  title={ExtDM: Distribution Extrapolation Diffusion Model for Video Prediction},
  author={Zhang, Zhicheng and Hu, Junyao and Cheng, Wentao and Paudel, Danda and Yang, Jufeng},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2024}
}