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pro vyhledávání: '"Zhou, Keyang"'
Generating realistic hand motion sequences in interaction with objects has gained increasing attention with the growing interest in digital humans. Prior work has illustrated the effectiveness of employing occupancy-based or distance-based virtual se
Externí odkaz:
http://arxiv.org/abs/2404.01758
We present TOCH, a method for refining incorrect 3D hand-object interaction sequences using a data prior. Existing hand trackers, especially those that rely on very few cameras, often produce visually unrealistic results with hand-object intersection
Externí odkaz:
http://arxiv.org/abs/2205.07982
Depth estimation, as a necessary clue to convert 2D images into the 3D space, has been applied in many machine vision areas. However, to achieve an entire surrounding 360-degree geometric sensing, traditional stereo matching algorithms for depth esti
Externí odkaz:
http://arxiv.org/abs/2108.08076
Most learning methods for 3D data (point clouds, meshes) suffer significant performance drops when the data is not carefully aligned to a canonical orientation. Aligning real world 3D data collected from different sources is non-trivial and requires
Externí odkaz:
http://arxiv.org/abs/2102.01161
Parametric models of humans, faces, hands and animals have been widely used for a range of tasks such as image-based reconstruction, shape correspondence estimation, and animation. Their key strength is the ability to factor surface variations into s
Externí odkaz:
http://arxiv.org/abs/2007.11341
Akademický článek
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Autor:
Zhou, Keyang, Kainz, Bernhard
Convolutional neural networks (CNNs) define the current state-of-the-art for image recognition. With their emerging popularity, especially for critical applications like medical image analysis or self-driving cars, confirmability is becoming an issue
Externí odkaz:
http://arxiv.org/abs/1801.01693
Publikováno v:
In Land Use Policy October 2021 109
Publikováno v:
In Journal of Transport Geography January 2020 82
Akademický článek
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