Zobrazeno 1 - 10
of 1 080
pro vyhledávání: '"Ma Jianxin"'
Publikováno v:
E3S Web of Conferences, Vol 476, p 01042 (2024)
High-strength concrete is a brittle material due to its low tensile strength. Splitting tensile strength of concrete can be used to determine the concrete tensile capacity. High-strength concrete has high bearing capacity and good durability during i
Externí odkaz:
https://doaj.org/article/445a4887c53b4bc497d0de57c21481e0
Generating photorealistic 3D faces from given conditions is a challenging task. Existing methods often rely on time-consuming one-by-one optimization approaches, which are not efficient for modeling the same distribution content, e.g., faces. Additio
Externí odkaz:
http://arxiv.org/abs/2312.13941
Autor:
Bai, Jinze, Bai, Shuai, Chu, Yunfei, Cui, Zeyu, Dang, Kai, Deng, Xiaodong, Fan, Yang, Ge, Wenbin, Han, Yu, Huang, Fei, Hui, Binyuan, Ji, Luo, Li, Mei, Lin, Junyang, Lin, Runji, Liu, Dayiheng, Liu, Gao, Lu, Chengqiang, Lu, Keming, Ma, Jianxin, Men, Rui, Ren, Xingzhang, Ren, Xuancheng, Tan, Chuanqi, Tan, Sinan, Tu, Jianhong, Wang, Peng, Wang, Shijie, Wang, Wei, Wu, Shengguang, Xu, Benfeng, Xu, Jin, Yang, An, Yang, Hao, Yang, Jian, Yang, Shusheng, Yao, Yang, Yu, Bowen, Yuan, Hongyi, Yuan, Zheng, Zhang, Jianwei, Zhang, Xingxuan, Zhang, Yichang, Zhang, Zhenru, Zhou, Chang, Zhou, Jingren, Zhou, Xiaohuan, Zhu, Tianhang
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our la
Externí odkaz:
http://arxiv.org/abs/2309.16609
In this study, we focus on the problem of 3D human mesh recovery from a single image under obscured conditions. Most state-of-the-art methods aim to improve 2D alignment technologies, such as spatial averaging and 2D joint sampling. However, they ten
Externí odkaz:
http://arxiv.org/abs/2307.16377
In this paper, we focus on the task of generalizable neural human rendering which trains conditional Neural Radiance Fields (NeRF) from multi-view videos of different characters. To handle the dynamic human motion, previous methods have primarily use
Externí odkaz:
http://arxiv.org/abs/2307.12291
Video-based 3D human pose and shape estimations are evaluated by intra-frame accuracy and inter-frame smoothness. Although these two metrics are responsible for different ranges of temporal consistency, existing state-of-the-art methods treat them as
Externí odkaz:
http://arxiv.org/abs/2303.14747
Autor:
Bai, Jinze, Men, Rui, Yang, Hao, Ren, Xuancheng, Dang, Kai, Zhang, Yichang, Zhou, Xiaohuan, Wang, Peng, Tan, Sinan, Yang, An, Cui, Zeyu, Han, Yu, Bai, Shuai, Ge, Wenbin, Ma, Jianxin, Lin, Junyang, Zhou, Jingren, Zhou, Chang
Generalist models, which are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model, have been explored recently. Being, hopefully, an alternative to approaching general-purpose AI, existing generalist models are
Externí odkaz:
http://arxiv.org/abs/2212.04408
Generative modeling of human motion has broad applications in computer animation, virtual reality, and robotics. Conventional approaches develop separate models for different motion synthesis tasks, and typically use a model of a small size to avoid
Externí odkaz:
http://arxiv.org/abs/2212.02837
Industrial recommender systems have been growing increasingly complex, may involve \emph{diverse domains} such as e-commerce products and user-generated contents, and can comprise \emph{a myriad of tasks} such as retrieval, ranking, explanation gener
Externí odkaz:
http://arxiv.org/abs/2205.08084
Autor:
Wang, Peng, Yang, An, Men, Rui, Lin, Junyang, Bai, Shuai, Li, Zhikang, Ma, Jianxin, Zhou, Chang, Zhou, Jingren, Yang, Hongxia
In this work, we pursue a unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization. We propose OFA, a Task-Agnostic and Modality-Agnostic framework that supports Task Comprehensiveness. OFA un
Externí odkaz:
http://arxiv.org/abs/2202.03052