Zobrazeno 1 - 10
of 4 251
pro vyhledávání: '"A. Ukita"'
This paper addresses a new virtual try-on problem of fitting any size of clothes to a reference person in the image domain. While previous image-based virtual try-on methods can produce highly natural try-on images, these methods fit the clothes on t
Externí odkaz:
http://arxiv.org/abs/2412.06201
Autor:
Yamazaki, Takeshi, Ishikawa, Ken-ichi, Ishizuka, Naruhito, Kuramashi, Yoshinobu, Namekawa, Yusuke, Taniguchi, Yusuke, Collaboration, Naoya Ukita for PACS
We calculate the form factors for the kaon semileptonic decay process using the PACS10 configurations, whose physical volume is more than (10 fm)$^4$ very close to the physical point. The configurations were generated with the Iwasaki gauge action an
Externí odkaz:
http://arxiv.org/abs/2412.05778
Autor:
Akita, Kazutoshi, Ukita, Norimichi
Super-resolution (SR) with arbitrary scale factor and cost-and-quality controllability at test time is essential for various applications. While several arbitrary-scale SR methods have been proposed, these methods require us to modify the model struc
Externí odkaz:
http://arxiv.org/abs/2412.05517
While burst LR images are useful for improving the SR image quality compared with a single LR image, prior SR networks accepting the burst LR images are trained in a deterministic manner, which is known to produce a blurry SR image. In addition, it i
Externí odkaz:
http://arxiv.org/abs/2403.19428
Autor:
Shimosato, Kodai, Ukita, Norimichi
This paper proposes a mask optimization method for improving the quality of object removal using image inpainting. While many inpainting methods are trained with a set of random masks, a target for inpainting may be an object, such as a person, in ma
Externí odkaz:
http://arxiv.org/abs/2403.15849
Autor:
Mori, Hiroshi, Ukita, Norimichi
A Recurrent Neural Network (RNN) for Video Super Resolution (VSR) is generally trained with randomly clipped and cropped short videos extracted from original training videos due to various challenges in learning RNNs. However, since this RNN is optim
Externí odkaz:
http://arxiv.org/abs/2403.15832
This paper proposes a depth estimation method using radar-image fusion by addressing the uncertain vertical directions of sparse radar measurements. In prior radar-image fusion work, image features are merged with the uncertain sparse depths measured
Externí odkaz:
http://arxiv.org/abs/2403.15787
In this paper, we analyze and discuss ShadowFormer in preparation for the NTIRE2023 Shadow Removal Challenge [1], implementing five key improvements: image alignment, the introduction of a perceptual quality loss function, the semi-automatic annotati
Externí odkaz:
http://arxiv.org/abs/2403.08995
This paper proposes Group Activity Feature (GAF) learning in which features of multi-person activity are learned as a compact latent vector. Unlike prior work in which the manual annotation of group activities is required for supervised learning, our
Externí odkaz:
http://arxiv.org/abs/2403.02753
Autor:
Taketsugu, Hiromu, Ukita, Norimichi
Human Pose (HP) estimation is actively researched because of its wide range of applications. However, even estimators pre-trained on large datasets may not perform satisfactorily due to a domain gap between the training and test data. To address this
Externí odkaz:
http://arxiv.org/abs/2311.05041