https://www.youtube.com/watch?v=JR5KL6Mo8tU
Compared Video Results for ICCV 2021 paper FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting

https://github.com/XiaoqiangZhou/FuseFormer-1
@InProceedings{Liu_2021_FuseFormer, title={FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting}, author={Liu, Rui and Deng, Hanming and Huang, Yangyi and Shi, Xiaoyu and Lu, Lewei and Sun, Wenxiu and Wang, Xiaogang and Dai, Jifeng and Li, Hongsheng}, booktitle = {International Conference on Computer Vision (ICCV)}, year={2021} }

https://arxiv.org/abs/2109.02974
Here we aim to tackle this problem by proposing FuseFormer, a Transformer model designed for video inpainting via fine-grained feature fusion based on novel Soft Split and Soft Composition operations. The soft split divides feature map into many patches with given overlapping interval. On the contrary, the soft composition operates by stitching

https://github.com/ruiliu-ai/FuseFormer
@InProceedings{Liu_2021_FuseFormer, title={FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting}, author={Liu, Rui and Deng, Hanming and Huang, Yangyi and Shi, Xiaoyu and Lu, Lewei and Sun, Wenxiu and Wang, Xiaogang and Dai, Jifeng and Li, Hongsheng}, booktitle = {International Conference on Computer Vision (ICCV)}, year={2021} }

https://52.152.142.11/content/ICCV2021/papers/Liu_FuseFormer_Fusing_Fine-Grained_Information_in_Transformers_for_Video_Inpainting_ICCV_2021_paper.pdf
el Transformer-based video inpainting framework endorsed by 2 carefully-designed soft operations, which improve the performance on both video restoration and object removal and make the inference much faster as well. 3. Method In this section we introduce our FuseFormer model for video inpainting. We start by proposing a simple Trans-

https://openaccess.thecvf.com/content/ICCV2021/html/Liu_FuseFormer_Fusing_Fine-Grained_Information_in_Transformers_for_Video_Inpainting_ICCV_2021_paper.html
These ICCV 2021 papers are the Open Access versions, provided by the Computer Vision Foundation. ... resulting in synthesizing vivid content for hole regions in videos. Moreover, in FuseFormer, we elaborately insert the soft composition and soft split into the feed-forward network, enabling the 1D linear layers to have the capability of

https://www.researchgate.net/publication/359001746_FuseFormer_Fusing_Fine-Grained_Information_in_Transformers_for_Video_Inpainting
The DAVIS includes 60 training videos and 90 testing videos. FuseFormer [9] is used to create 50 video clips from the test set that we also utilized to compute the performance. We build stationary

https://github.com/yjla1109/FuseFormer_classification
official Pytorch implementation of ICCV 2021 paper FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting. - GitHub - yjla1109/FuseFormer_classification: official Pytorch implementation of ICCV 2021 paper FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting.

http://export.arxiv.org/abs/2109.02974
Therefore, sub-patch level information interaction is enabled for more effective feature propagation between neighboring patches, resulting in synthesizing vivid content for hole regions in videos. Moreover, in FuseFormer, we elaborately insert the soft composition and soft split into the feed-forward network, enabling the 1D linear layers to

https://www.researchgate.net/publication/354435008_FuseFormer_Fusing_Fine-Grained_Information_in_Transformers_for_Video_Inpainting
Here we aim to tackle this problem by proposing FuseFormer, a Transformer model designed for video inpainting via fine-grained feature fusion based on novel Soft Split and Soft Composition

https://www.semanticscholar.org/paper/FuseFormer%3A-Fusing-Fine-Grained-Information-in-for-Liu-Deng/7809f2f509b5bd1ce3d8b9a89ef25a755dd729a2
This work proposes FuseFormer, a Transformer model designed for video inpainting via fine-grained feature fusion based on novel Soft Split and Soft Composition operations, which surpasses state-of-the-art methods in both quantitative and qualitative evaluations. Transformer, as a strong and flexible architecture for modelling long-range relations, has been widely explored in vision tasks.

https://pythonawesome.com/fuseformer-fusing-fine-grained-information-in-transformers-for-video-inpainting/
Official code release for 3DV 2021 paper Human Performance Capture from Monocular Video in the Wild 11 February 2022 Python Awesome is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.

https://github.ink/ruiliu-ai/FuseFormer
official Pytorch implementation of ICCV 2021 paper FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting. - GitHub - ruiliu-ai/FuseFormer: official Pytorch implementation

https://paperswithcode.com/method/fuseformer
FuseFormer is a Transformer-based model designed for video inpainting via fine-grained feature fusion based on novel Soft Split and Soft Composition operations. The soft split divides feature map into many patches with given overlapping interval while the soft composition stitches them back into a whole feature map where pixels in overlapping regions are summed up.

https://openaccess.thecvf.com/ICCV2021
These ICCV 2021 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. This material is presented to ensure timely dissemination of scholarly and technical work.

https://paperswithcode.com/paper/fuseformer-a-transformer-for-visual-and
The multi-scale features are fused using a combination of Convolutional Neural Networks (CNNs) and Transformers. The CNNs are utilized to capture local features, while the Transformer handles the integration of general context features. Through extensive experiments on various benchmark datasets, our proposed method, along with the novel loss

https://openaccess.thecvf.com/content/ICCV2021W/CVEU/html/Neimark_Video_Transformer_Network_ICCVW_2021_paper.html
These ICCV 2021 workshop papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. This material is presented to ensure timely dissemination of scholarly and technical work.

https://github.com/LokiXun/diffusion_video_paper_list/blob/master/2021_09_ICCV_FuseFormer--Fusing-Fine-Grained-Information-in-Transformers-for-Video-Inpainting.pdf
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https://www.youtube.com/watch?v=N53jeS2atPc
This is the 5 minute video for our ICCV 2021 paper:"Hybrid Neural Fusion for Full-frame Video Stabilization"Project Page: https://alex04072000.github.io/FuSt

https://git.codeproxy.net/ruiliu-ai/FuseFormer
official Pytorch implementation of ICCV 2021 paper FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting. - ruiliu-ai/FuseFormer

https://github.com/ruiliu-ai/FuseFormer/blob/master/model/fuseformer.py
official Pytorch implementation of ICCV 2021 paper FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting. - ruiliu-ai/FuseFormer

https://openaccess.thecvf.com/content/ICCV2021/html/Weng_Event-Based_Video_Reconstruction_Using_Transformer_ICCV_2021_paper
These ICCV 2021 papers are the Open Access versions, provided by the Computer ... which is crucial to many vision tasks. In this paper, we present a hybrid CNN-Transformer network for event-based video reconstruction (ET-Net), which merits the fine local information from CNN and global contexts from Transformer. ... Experimental results