Human and Scene Motion Deblurring Using Pseudo-Blur Synthesizer
TLDR
In this article, an on-the-fly blurry data augmenter that can be run during training and test stages is proposed. And the proposed deblurring module is also equipped with hand-crafted prior extracted using the state-of-theart human body statistical model.Abstract:
Present-day deep learning-based motion deblurring methods utilize the pair of synthetic blur and sharp data to regress any particular framework. This task is designed for directly translating a blurry image input into its restored version as output. The aforementioned approach relies heavily on the quality of the synthetic blurry data, which are only available before the training stage. Handling this issue by providing a large amount of data is expensive for common usage. We answer this challenge by providing an on- the-fly blurry data augmenter that can be run during training and test stages. To fully utilize it, we incorporate an unorthodox scheme of deblurring framework that employs the sequence of blur-deblur-reblur-deblur steps. The reblur step is assisted by a reblurring module (synthesizer) that provides the reblurred version (pseudo-blur) of its sharp or deblurred counterpart. The proposed module is also equipped with hand-crafted prior extracted using the state-of-the-art human body statistical model. This prior is employed to map human and non-human regions during adversarial learning to fully perceive the characteristics of human-articulated and scene motion blurs. By engaging this approach, our deblurring module becomes adaptive and achieves superior outcomes compared to recent state-of-the-art deblurring algorithms.read more
Citations
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Proceedings ArticleDOI
3D Body Reconstruction Revisited: Exploring the Test-time 3D Body Mesh Refinement Strategy via Surrogate Adaptation
TL;DR: This work proposes a test-time adaptation strategy that fine-tunes the surrogate 3D body module using reliable virtual data and can revisit the prior state-of-the-art works and improve their performances directly in the test phase.
Journal ArticleDOI
Human from Blur: Human Pose Tracking from Blurry Images
TL;DR: Zhang et al. as mentioned in this paper proposed a method to estimate 3D human poses from substantially blurred images by backpropagating the pixel-wise reprojection error to recover the best human motion representation that explains a single or multiple input images.
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