1 code implementation • 8 Apr 2024 • Jiaxiu Jiang, Yabo Zhang, Kailai Feng, Xiaohe Wu, WangMeng Zuo
Customized text-to-image generation aims to synthesize instantiations of user-specified concepts and has achieved unprecedented progress in handling individual concept.
1 code implementation • 8 Mar 2024 • Yabo Zhang, Yuxiang Wei, Xianhui Lin, Zheng Hui, Peiran Ren, Xuansong Xie, Xiangyang Ji, WangMeng Zuo
Different from conventional T2V sampling (i. e., temporal and spatial modeling), VideoElevator explicitly decomposes each sampling step into temporal motion refining and spatial quality elevating.
no code implementations • 31 Aug 2023 • Minheng Ni, Yabo Zhang, Kailai Feng, Xiaoming Li, Yiwen Guo, WangMeng Zuo
In this work, we introduce a novel Referring Diffusional segmentor (Ref-Diff) for this task, which leverages the fine-grained multi-modal information from generative models.
1 code implementation • 27 Aug 2023 • Mingshuai Yao, Yabo Zhang, Xianhui Lin, Xiaoming Li, WangMeng Zuo
In this paper, we propose a VQGAN-based framework (i. e., VQ-Font) to enhance glyph fidelity through token prior refinement and structure-aware enhancement.
1 code implementation • 22 May 2023 • Yabo Zhang, Yuxiang Wei, Dongsheng Jiang, Xiaopeng Zhang, WangMeng Zuo, Qi Tian
Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temporal modeling.
no code implementations • 3 Apr 2023 • Yabo Zhang, ZiHao Wang, Jun Hao Liew, Jingjia Huang, Manyu Zhu, Jiashi Feng, WangMeng Zuo
In this work, we investigate performing semantic segmentation solely through the training on image-sentence pairs.
1 code implementation • ICCV 2023 • Yuxiang Wei, Yabo Zhang, Zhilong Ji, Jinfeng Bai, Lei Zhang, WangMeng Zuo
In addition to the unprecedented ability in imaginary creation, large text-to-image models are expected to take customized concepts in image generation.
1 code implementation • 18 Jul 2022 • Yabo Zhang, Mingshuai Yao, Yuxiang Wei, Zhilong Ji, Jinfeng Bai, WangMeng Zuo
In this paper, we present a novel one-shot generative domain adaption method, i. e., DiFa, for diverse generation and faithful adaptation.