Zobrazeno 1 - 10
of 635
pro vyhledávání: '"Wang, Xintao"'
Autor:
Wang, Zhouxia, Zhang, Jiawei, Wang, Xintao, Chen, Tianshui, Shan, Ying, Wang, Wenping, Luo, Ping
Publikováno v:
IEEE Trans Image Process. 2024;33:5676-5687. Epub 2024 Oct 9. PMID: 39316481
Recent progress in blind face restoration has resulted in producing high-quality restored results for static images. However, efforts to extend these advancements to video scenarios have been minimal, partly because of the absence of benchmarks that
Externí odkaz:
http://arxiv.org/abs/2410.11828
Autor:
Wu, Tao, Zhang, Yong, Wang, Xintao, Zhou, Xianpan, Zheng, Guangcong, Qi, Zhongang, Shan, Ying, Li, Xi
Customized video generation aims to generate high-quality videos guided by text prompts and subject's reference images. However, since it is only trained on static images, the fine-tuning process of subject learning disrupts abilities of video diffus
Externí odkaz:
http://arxiv.org/abs/2408.13239
Traditional visual storytelling is complex, requiring specialized knowledge and substantial resources, yet often constrained by human creativity and creation precision. While Large Language Models (LLMs) enhance visual storytelling, current approache
Externí odkaz:
http://arxiv.org/abs/2408.11801
Autor:
Ju, Xuan, Gao, Yiming, Zhang, Zhaoyang, Yuan, Ziyang, Wang, Xintao, Zeng, Ailing, Xiong, Yu, Xu, Qiang, Shan, Ying
Sora's high-motion intensity and long consistent videos have significantly impacted the field of video generation, attracting unprecedented attention. However, existing publicly available datasets are inadequate for generating Sora-like videos, as th
Externí odkaz:
http://arxiv.org/abs/2407.06358
Autor:
Xu, Rui, Lu, Dakuan, Tan, Xiaoyu, Wang, Xintao, Yuan, Siyu, Chen, Jiangjie, Chu, Wei, Xu, Yinghui
Large language models~(LLMs) have demonstrated impressive performance in various applications, among which role-playing language agents (RPLAs) have engaged a broad user base. Now, there is a growing demand for RPLAs that represent Key Opinion Leader
Externí odkaz:
http://arxiv.org/abs/2407.05305
Autor:
Xia, Sirui, Wang, Xintao, Liang, Jiaqing, Zhang, Yifei, Zhou, Weikang, Deng, Jiaji, Yu, Fei, Xiao, Yanghua
Retrieval-Augmented Generation (RAG) has been widely adopted to enhance Large Language Models (LLMs) in knowledge-intensive tasks. Recently, Attributed Text Generation (ATG) has attracted growing attention, which provides citations to support the mod
Externí odkaz:
http://arxiv.org/abs/2407.01796
Large Language Models (LLMs) have exhibited impressive proficiency in various natural language processing (NLP) tasks, which involve increasingly complex reasoning. Knowledge reasoning, a primary type of reasoning, aims at deriving new knowledge from
Externí odkaz:
http://arxiv.org/abs/2407.00653
Autor:
Ran, Yiting, Wang, Xintao, Xu, Rui, Yuan, Xinfeng, Liang, Jiaqing, Yang, Deqing, Xiao, Yanghua
Role-playing agents (RPA) have been a popular application area for large language models (LLMs), attracting significant interest from both industry and academia.While existing RPAs well portray the characters' knowledge and tones, they face challenge
Externí odkaz:
http://arxiv.org/abs/2406.18921
Autor:
Li, Yaowei, Wang, Xintao, Zhang, Zhaoyang, Wang, Zhouxia, Yuan, Ziyang, Xie, Liangbin, Zou, Yuexian, Shan, Ying
Filmmaking and animation production often require sophisticated techniques for coordinating camera transitions and object movements, typically involving labor-intensive real-world capturing. Despite advancements in generative AI for video creation, a
Externí odkaz:
http://arxiv.org/abs/2406.15339
Multi-Modal Knowledge Graphs (MMKGs) have proven valuable for various downstream tasks. However, scaling them up is challenging because building large-scale MMKGs often introduces mismatched images (i.e., noise). Most entities in KGs belong to the lo
Externí odkaz:
http://arxiv.org/abs/2406.10902