Temporal Modulation Network for Controllable Space-Time Video Super-Resolution

• URL: http://arxiv.org/abs/2104.10642v1
• Date: Wed, 21 Apr 2021 17:10:53 GMT
• Title: Temporal Modulation Network for Controllable Space-Time Video Super-Resolution
• Authors: Gang Xu and Jun Xu and Zhen Li and Liang Wang and Xing Sun and Ming-Ming Cheng
• Abstract summary: Space-time video super-resolution aims to increase the spatial and temporal resolutions of low-resolution and low-frame-rate videos. Deformable convolution based methods have achieved promising STVSR performance, but they could only infer the intermediate frame pre-defined in the training stage. We propose a Temporal Modulation Network (TMNet) to interpolate arbitrary intermediate frame(s) with accurate high-resolution reconstruction.
• Score: 66.06549492893947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Space-time video super-resolution (STVSR) aims to increase the spatial and temporal resolutions of low-resolution and low-frame-rate videos. Recently, deformable convolution based methods have achieved promising STVSR performance, but they could only infer the intermediate frame pre-defined in the training stage. Besides, these methods undervalued the short-term motion cues among adjacent frames. In this paper, we propose a Temporal Modulation Network (TMNet) to interpolate arbitrary intermediate frame(s) with accurate high-resolution reconstruction. Specifically, we propose a Temporal Modulation Block (TMB) to modulate deformable convolution kernels for controllable feature interpolation. To well exploit the temporal information, we propose a Locally-temporal Feature Comparison (LFC) module, along with the Bi-directional Deformable ConvLSTM, to extract short-term and long-term motion cues in videos. Experiments on three benchmark datasets demonstrate that our TMNet outperforms previous STVSR methods. The code is available at https://github.com/CS-GangXu/TMNet.

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