The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Depth-Aware Video Frame Interpolation
Abstract
Video frame interpolation aims to synthesize nonexistent frames in-between the original frames. While significant advances have been made from the recent deep convolutional neural networks, the quality of interpolation is often reduced due to large object motion or occlusion. In this work, we propose a video frame interpolation method which explicitly detects the occlusion by exploring the depth information. Specifically, we develop a depth-aware flow projection layer to synthesize intermediate flows that preferably sample closer objects than farther ones. In addition, we learn hierarchical features to gather contextual information from neighboring pixels. The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame. Our model is compact, efficient, and fully differentiable. Quantitative and qualitative results demonstrate that the proposed model performs favorably against state-of-the-art frame interpolation methods on a wide variety of datasets. The source code and pre-trained model are available at https://github.com/baowenbo/DAIN.
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Cites
Deep Residual Learning for Image Recognition
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Self-citationU-Net: Convolutional Networks for Biomedical Image Segmentation
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Ucf101: A dataset of 101 human actions classes from videos in the wild
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Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
Eigen, D., Puhrsch, C., & Fergus, R. (2014) · arXiv · doi:10.48550/arxiv.1406.2283 · Cited by 3,442
Unsupervised Learning of Depth and Ego-Motion from Video
Zhou, T., Brown, M., Snavely, N., & Lowe, D. G. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.700 · Cited by 2,910
Eigen, D., & Fergus, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.304 · Cited by 2,862
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
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Deep Ordinal Regression Network for Monocular Depth Estimation
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Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes
Li, Z., Niklaus, S., Snavely, N., & Wang, O. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00643 · Cited by 674
Channel Attention Is All You Need for Video Frame Interpolation
Choi, M., Kim, H., Han, B., Xu, N., & Lee, K. M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6693 · Cited by 328
StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2
Skorokhodov, I., Tulyakov, S., & Elhoseiny, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00361 · Cited by 199
DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion
Karras, J., Holynski, A., Wang, T.-C., & Kemelmacher-Shlizerman, I. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02073 · Cited by 137
Priessner, M., Gaboriau, D. C. A., Sheridan, A., Lenn, T., Garzon-Coral, C., Dunn, A. R., Chubb, J. R., Tousley, A. M., Majzner, R. G., Manor, U., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02138-w · Cited by 35
Metadata
- Authors
- Bao, W., Lai, W.-S., Ma, C., Zhang, X., Gao, Z., & Yang, M.-H.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 3698-3707
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2949258649
- Cited by
- 554
- References
- 40
- FWCI
- 22.94
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Bao, W., Lai, W.-S., Ma, C., Zhang, X., Gao, Z., & Yang, M.-H. (2019). Depth-Aware Video Frame Interpolation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00382
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