Zobrazeno 1 - 10
of 256
pro vyhledávání: '"Huang Jiancheng"'
Text-conditional image editing is a practical AIGC task that has recently emerged with great commercial and academic value. For real image editing, most diffusion model-based methods use DDIM Inversion as the first stage before editing. However, DDIM
Externí odkaz:
http://arxiv.org/abs/2412.11152
Autor:
Zhou, Donghao, Huang, Jiancheng, Bai, Jinbin, Wang, Jiaze, Chen, Hao, Chen, Guangyong, Hu, Xiaowei, Heng, Pheng-Ann
Recent text-to-image models generate high-quality images from text prompts but lack precise control over specific components within visual concepts. Therefore, we introduce component-controllable personalization, a new task that allows users to custo
Externí odkaz:
http://arxiv.org/abs/2410.13370
In this paper, we introduce MRStyle, a comprehensive framework that enables color style transfer using multi-modality reference, including image and text. To achieve a unified style feature space for both modalities, we first develop a neural network
Externí odkaz:
http://arxiv.org/abs/2409.05250
Autor:
Qiu, Jing, Huang, Jiancheng, Zhang, Xiangdong, Lin, Zeng, Pan, Minglei, Liu, Zengding, Miao, Fen
Physics-informed deep learning has been developed as a novel paradigm for learning physical dynamics recently. While general physics-informed deep learning methods have shown early promise in learning fluid dynamics, they are difficult to generalize
Externí odkaz:
http://arxiv.org/abs/2406.03711
In this paper, we introduce Matten, a cutting-edge latent diffusion model with Mamba-Attention architecture for video generation. With minimal computational cost, Matten employs spatial-temporal attention for local video content modeling and bidirect
Externí odkaz:
http://arxiv.org/abs/2405.03025
Autor:
Liu, Xiaoning, Wu, Zongwei, Li, Ao, Vasluianu, Florin-Alexandru, Zhang, Yulun, Gu, Shuhang, Zhang, Le, Zhu, Ce, Timofte, Radu, Jin, Zhi, Wu, Hongjun, Wang, Chenxi, Ling, Haitao, Cai, Yuanhao, Bian, Hao, Zheng, Yuxin, Lin, Jing, Yuille, Alan, Shao, Ben, Guo, Jin, Liu, Tianli, Wu, Mohao, Feng, Yixu, Hou, Shuo, Lin, Haotian, Zhu, Yu, Wu, Peng, Dong, Wei, Sun, Jinqiu, Zhang, Yanning, Yan, Qingsen, Zou, Wenbin, Yang, Weipeng, Li, Yunxiang, Wei, Qiaomu, Ye, Tian, Chen, Sixiang, Zhang, Zhao, Zhao, Suiyi, Wang, Bo, Luo, Yan, Zuo, Zhichao, Wang, Mingshen, Wang, Junhu, Wei, Yanyan, Sun, Xiaopeng, Gao, Yu, Huang, Jiancheng, Chen, Hongming, Chen, Xiang, Tang, Hui, Chen, Yuanbin, Zhou, Yuanbo, Dai, Xinwei, Qiu, Xintao, Deng, Wei, Gao, Qinquan, Tong, Tong, Li, Mingjia, Hu, Jin, He, Xinyu, Guo, Xiaojie, Sabarinathan, Uma, K, Sasithradevi, A, Bama, B Sathya, Roomi, S. Mohamed Mansoor, Srivatsav, V., Wang, Jinjuan, Sun, Long, Chen, Qiuying, Shao, Jiahong, Zhang, Yizhi, Conde, Marcos V., Feijoo, Daniel, Benito, Juan C., García, Alvaro, Lee, Jaeho, Kim, Seongwan, A, Sharif S M, Khujaev, Nodirkhuja, Tsoy, Roman, Murtaza, Ali, Khairuddin, Uswah, Faudzi, Ahmad 'Athif Mohd, Malagi, Sampada, Joshi, Amogh, Akalwadi, Nikhil, Desai, Chaitra, Tabib, Ramesh Ashok, Mudenagudi, Uma, Lian, Wenyi, Lian, Wenjing, Kalyanshetti, Jagadeesh, Aralikatti, Vijayalaxmi Ashok, Yashaswini, Palani, Upasi, Nitish, Hegde, Dikshit, Patil, Ujwala, C, Sujata, Yan, Xingzhuo, Hao, Wei, Fu, Minghan, choksy, Pooja, Sarvaiya, Anjali, Upla, Kishor, Raja, Kiran, Yan, Hailong, Zhang, Yunkai, Li, Baiang, Zhang, Jingyi, Zheng, Huan
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and vi
Externí odkaz:
http://arxiv.org/abs/2404.14248
Autor:
Huang, Yi, Huang, Jiancheng, Liu, Yifan, Yan, Mingfu, Lv, Jiaxi, Liu, Jianzhuang, Xiong, Wei, Zhang, He, Chen, Shifeng, Cao, Liangliang
Denoising diffusion models have emerged as a powerful tool for various image generation and editing tasks, facilitating the synthesis of visual content in an unconditional or input-conditional manner. The core idea behind them is learning to reverse
Externí odkaz:
http://arxiv.org/abs/2402.17525
Autor:
Chen, Songyan, Huang, Jiancheng
Text-conditional image editing based on large diffusion generative model has attracted the attention of both the industry and the research community. Most existing methods are non-reference editing, with the user only able to provide a source image a
Externí odkaz:
http://arxiv.org/abs/2401.03433
GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning
Autor:
Lv, Jiaxi, Huang, Yi, Yan, Mingfu, Huang, Jiancheng, Liu, Jianzhuang, Liu, Yifan, Wen, Yafei, Chen, Xiaoxin, Chen, Shifeng
Recent advances in text-to-video generation have harnessed the power of diffusion models to create visually compelling content conditioned on text prompts. However, they usually encounter high computational costs and often struggle to produce videos
Externí odkaz:
http://arxiv.org/abs/2311.12631
In document processing, seal-related tasks have very large commercial applications, such as seal segmentation, seal authenticity discrimination, seal removal, and text recognition under seals. However, these seal-related tasks are highly dependent on
Externí odkaz:
http://arxiv.org/abs/2310.00546