Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Yang, Dingdong"'
We propose GALA, a novel representation of 3D shapes that (i) excels at capturing and reproducing complex geometry and surface details, (ii) is computationally efficient, and (iii) lends itself to 3D generative modelling with modern, diffusion-based
Externí odkaz:
http://arxiv.org/abs/2410.10037
We present BRICS, a bi-level feature representation for image collections, which consists of a key code space on top of a feature grid space. Specifically, our representation is learned by an autoencoder to encode images into continuous key codes, wh
Externí odkaz:
http://arxiv.org/abs/2305.18601
Autor:
Zhang, Jianfeng, Jiang, Zihang, Yang, Dingdong, Xu, Hongyi, Shi, Yichun, Song, Guoxian, Xu, Zhongcong, Wang, Xinchao, Feng, Jiashi
Unsupervised generation of 3D-aware clothed humans with various appearances and controllable geometries is important for creating virtual human avatars and other AR/VR applications. Existing methods are either limited to rigid object modeling, or not
Externí odkaz:
http://arxiv.org/abs/2211.14589
Night imaging with modern smartphone cameras is troublesome due to low photon count and unavoidable noise in the imaging system. Directly adjusting exposure time and ISO ratings cannot obtain sharp and noise-free images at the same time in low-light
Externí odkaz:
http://arxiv.org/abs/2207.03294
We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN app
Externí odkaz:
http://arxiv.org/abs/1901.09024
We propose a novel hierarchical approach for text-to-image synthesis by inferring semantic layout. Instead of learning a direct mapping from text to image, our algorithm decomposes the generation process into multiple steps, in which it first constru
Externí odkaz:
http://arxiv.org/abs/1801.05091
We present BRIGHT, a bi-level feature representation for an image collection, consisting of a per-image latent space on top of a multi-scale feature grid space. Our representation is learned by an autoencoder to encode images into continuous key code
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f55785b7f50faef7ffafa405adabcfa3
http://arxiv.org/abs/2305.18601
http://arxiv.org/abs/2305.18601
Autor:
Kye, Jongwook, Owa, Soichi, Sim, Woojoo, Lee, Kibok, Yang, Dingdong, Jeong, Jaeseung, Hong, Ji-Suk, Lee, Sooryong, Lee, Honglak
Publikováno v:
Proceedings of SPIE; June 2019, Vol. 10961 Issue: 1 p1096105-1096105-13
