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pro vyhledávání: '"Basak, Shubhajit"'
While current research predominantly focuses on image-based colorization, the domain of video-based colorization remains relatively unexplored. Most existing video colorization techniques operate on a frame-by-frame basis, often overlooking the criti
Externí odkaz:
http://arxiv.org/abs/2405.05707
Autor:
Basak, Shubhajit, Mangapuram, Sathish, Costache, Gabriel, McDonnell, Rachel, Schukat, Michael
The incorporation of 3D data in facial analysis tasks has gained popularity in recent years. Though it provides a more accurate and detailed representation of the human face, accruing 3D face data is more complex and expensive than 2D face images. Ei
Externí odkaz:
http://arxiv.org/abs/2308.15170
Autor:
Bigioi, Dan, Basak, Shubhajit, Stypułkowski, Michał, Zięba, Maciej, Jordan, Hugh, McDonnell, Rachel, Corcoran, Peter
Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diffusion model. Given a video of a talking person, and a separate auditor
Externí odkaz:
http://arxiv.org/abs/2301.04474
Autor:
Bigioi, Dan, Basak, Shubhajit, Stypułkowski, Michał, Zieba, Maciej, Jordan, Hugh, McDonnell, Rachel, Corcoran, Peter
Publikováno v:
In Image and Vision Computing February 2024 142
Recent advances in deep learning methods have increased the performance of face detection and recognition systems. The accuracy of these models relies on the range of variation provided in the training data. Creating a dataset that represents all var
Externí odkaz:
http://arxiv.org/abs/2006.11757
In this paper, we explore how synthetically generated 3D face models can be used to construct a high accuracy ground truth for depth. This allows us to train the Convolutional Neural Networks (CNN) to solve facial depth estimation problems. These mod
Externí odkaz:
http://arxiv.org/abs/2003.06211
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Publikováno v:
In Neural Networks December 2022 156:108-122
Publikováno v:
Chemical Communications; 12/25/2024, Vol. 60 Issue 99, p14818-14821, 4p
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