Softmax Splatting for Video Frame Interpolation
Published In
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Document Type
Citation
Publication Date
6-1-2020
Abstract
Differentiable image sampling in the form of backward warping has seen broad adoption in tasks like depth estimation and optical flow prediction. In contrast, how to perform forward warping has seen less attention, partly due to additional challenges such as resolving the conflict of mapping multiple pixels to the same target location in a differentiable way. We propose softmax splatting to address this paradigm shift and show its effectiveness on the application of frame interpolation. Specifically, given two input frames, we forward-warp the frames and their feature pyramid representations based on an optical flow estimate using softmax splatting. In doing so, the softmax splatting seamlessly handles cases where multiple source pixels map to the same target location. We then use a synthesis network to predict the interpolation result from the warped representations. Our softmax splatting allows us to not only interpolate frames at an arbitrary time but also to fine tune the feature pyramid and the optical flow. We show that our synthesis approach, empowered by softmax splatting, achieves new state-of-the-art results for video frame interpolation.
Locate the Document
DOI
10.1109/CVPR42600.2020.00548
Persistent Identifier
https://archives.pdx.edu/ds/psu/34773
Publisher
IEEE
Citation Details
Niklaus, S., & Liu, F. (2020). Softmax Splatting for Video Frame Interpolation. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/cvpr42600.2020.00548