STTN | [ECCV'2020] STTN: Learning Joint Spatial-Temporal Transformations for Video Inpainting

by researchmm Jupyter Notebook Version: Current License: No License

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kandi X-RAY | STTN Summary

STTN is a Jupyter Notebook library. STTN has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

High-quality video inpainting that completes missing regions in video frames is a promising yet challenging task. In this paper, we propose to learn a joint Spatial-Temporal Transformer Network (STTN) for video inpainting. Specifically, we simultaneously fill missing regions in all input frames by the proposed multi-scale patch-based attention modules. STTN is optimized by a spatial-temporal adversarial loss. To show the superiority of the proposed model, we conduct both quantitative and qualitative evaluations by using standard stationary masks and more realistic moving object masks.

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STTN has a low active ecosystem.

It has 367 star(s) with 71 fork(s). There are 20 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 4 have been closed. On average issues are closed in 10 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of STTN is current.

Quality

STTN has no bugs reported.

Security

STTN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

STTN does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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STTN releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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STTN Key Features

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STTN Examples and Code Snippets

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Install STTN

We build our project based on Pytorch and Python. For the full set of required Python packages, we suggest create a Conda environment from the provided YAML, e.g.

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For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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