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Motion-Aware Feature Enhancement Network for Video Prediction.
- Source :
-
IEEE Transactions on Circuits & Systems for Video Technology . Feb2021, Vol. 31 Issue 2, p688-700. 13p. - Publication Year :
- 2021
-
Abstract
- Video prediction is challenging, due to the pixel-level precision requirement and the difficulty in capturing scene dynamics. Most approaches tackle the problems by pixel-level reconstruction objectives and two decomposed branches, which still suffer from blurry generations or dramatic degradations in long-term prediction. In this paper, we propose a Motion-Aware Feature Enhancement (MAFE) network for video prediction to produce realistic future frames and achieve relatively long-term predictions. First, a Channel-wise and Spatial Attention (CSA) module is designed to extract motion-aware features, which enhances the contribution of important motion details during encoding, and subsequently improves the discriminability of attention map for the frame refinement. Second, a Motion Perceptual Loss (MPL) is proposed to guide the learning of temporal cues, which benefits to robust long-term video prediction. Extensive experiments on three human activity video datasets: KTH, Human3.6M, and PennAction demonstrate the effectiveness of the proposed video prediction model compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10518215
- Volume :
- 31
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Circuits & Systems for Video Technology
- Publication Type :
- Academic Journal
- Accession number :
- 148595704
- Full Text :
- https://doi.org/10.1109/TCSVT.2020.2987141