  The spatio-temporal information among video sequences is significant for
video super-resolution (SR). However, the spatio-temporal information cannot be
fully used by existing video SR methods since spatial feature extraction and
temporal motion compensation are usually performed sequentially. In this paper,
we propose a deformable 3D convolution network (D3Dnet) to incorporate
spatio-temporal information from both spatial and temporal dimensions for video
SR. Specifically, we introduce deformable 3D convolution (D3D) to integrate
deformable convolution with 3D convolution, obtaining both superior
spatio-temporal modeling capability and motion-aware modeling flexibility.
Extensive experiments have demonstrated the effectiveness of D3D in exploiting
spatio-temporal information. Comparative results show that our network achieves
state-of-the-art SR performance. Code is available at:
https://github.com/XinyiYing/D3Dnet.
