Lightweight Stochastic Video Prediction via Hybrid Warping

• URL: http://arxiv.org/abs/2412.03061v1
• Date: Wed, 04 Dec 2024 06:33:27 GMT
• Title: Lightweight Stochastic Video Prediction via Hybrid Warping
• Authors: Kazuki Kotoyori, Shota Hirose, Heming Sun, Jiro Katto,
• Abstract summary: Accurate video prediction by deep neural networks, especially for dynamic regions, is a challenging task in computer vision for critical applications such as autonomous driving, remote working, and telemedicine. We propose a novel long-term complexity video prediction model that focuses on dynamic regions by employing a hybrid warping strategy. Considering real-time predictions, we introduce a MobileNet-based lightweight architecture into our model.
• Score: 10.448675566568086
• License:
• Abstract: Accurate video prediction by deep neural networks, especially for dynamic regions, is a challenging task in computer vision for critical applications such as autonomous driving, remote working, and telemedicine. Due to inherent uncertainties, existing prediction models often struggle with the complexity of motion dynamics and occlusions. In this paper, we propose a novel stochastic long-term video prediction model that focuses on dynamic regions by employing a hybrid warping strategy. By integrating frames generated through forward and backward warpings, our approach effectively compensates for the weaknesses of each technique, improving the prediction accuracy and realism of moving regions in videos while also addressing uncertainty by making stochastic predictions that account for various motions. Furthermore, considering real-time predictions, we introduce a MobileNet-based lightweight architecture into our model. Our model, called SVPHW, achieves state-of-the-art performance on two benchmark datasets.

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