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Related Experiment Video

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Profiling Maternal Behavior Responses During Whole-Brain Imaging

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Consistent Video Style Transfer via Relaxation and Regularization.

Wenjing Wang, Shuai Yang, Jizheng Xu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 23, 2020

Summary

This summary is machine-generated.

This study introduces a novel approach for temporally consistent neural style transfer in videos. By relaxing objectives and introducing new regularization, it achieves robust stylization even with complex motion.

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Published on: March 19, 2016

Area of Science:

Computer Vision
Artificial Intelligence

Background:

Neural style transfer is popular for images but challenging for videos.
Existing video style transfer methods struggle with motion and complex variations.

Purpose of the Study:

To develop a temporally consistent neural style transfer method for videos.
To address limitations of current methods in handling motion and variations.

Main Methods:

Jointly considering stylization and temporal consistency.
Relaxing the objective function for motion robustness.
Proposing a novel formulation and regularization for temporal consistency.
Designing a zero-shot video style transfer framework with a dynamic inter-channel adjustment module.

Main Results:

The proposed method achieves stylization robust to inter-frame variations without degrading subjective quality.
The new regularization balances spatial and temporal performance effectively.
Quantitative and qualitative results show superiority over state-of-the-art methods.

Conclusions:

The developed framework offers superior temporally consistent video style transfer.
The approach effectively handles challenges posed by strong motions and complex variations in real videos.