no code implementations • 23 Apr 2024 • Weifeng Chen, Jiacheng Zhang, Jie Wu, Hefeng Wu, Xuefeng Xiao, Liang Lin
The rapid development of diffusion models has triggered diverse applications.
1 code implementation • 15 Apr 2024 • Weifeng Chen, Tao Gu, Yuhao Xu, Chengcai Chen
We propose Magic Clothing, a latent diffusion model (LDM)-based network architecture for an unexplored garment-driven image synthesis task.
3 code implementations • 4 Mar 2024 • Yuhao Xu, Tao Gu, Weifeng Chen, Chengcai Chen
We present OOTDiffusion, a novel network architecture for realistic and controllable image-based virtual try-on (VTON).
no code implementations • 9 Feb 2024 • Siming Yan, Min Bai, Weifeng Chen, Xiong Zhou, QiXing Huang, Li Erran Li
By combining natural language understanding, generation capabilities, and breadth of knowledge of large language models with image perception, recent large vision language models (LVLMs) have shown unprecedented visual reasoning capabilities.
no code implementations • 18 Jan 2024 • Jie Qin, Jie Wu, Weifeng Chen, Yuxi Ren, Huixia Li, Hefeng Wu, Xuefeng Xiao, Rui Wang, Shilei Wen
Diffusion models have opened up new avenues for the field of image generation, resulting in the proliferation of high-quality models shared on open-source platforms.
no code implementations • 12 Jan 2024 • Shengyi Qian, Weifeng Chen, Min Bai, Xiong Zhou, Zhuowen Tu, Li Erran Li
Affordance grounding refers to the task of finding the area of an object with which one can interact.
1 code implementation • 15 Nov 2023 • Hefeng Wu, Weifeng Chen, Zhibin Liu, Tianshui Chen, Zhiguang Chen, Liang Lin
Moreover, we propose a proximity data generation (PDG) module to automatically produce more diverse data for cross-modal training.
1 code implementation • 23 May 2023 • Weifeng Chen, Yatai Ji, Jie Wu, Hefeng Wu, Pan Xie, Jiashi Li, Xin Xia, Xuefeng Xiao, Liang Lin
Based on a pre-trained conditional text-to-image (T2I) diffusion model, our model aims to generate videos conditioned on a sequence of control signals, such as edge or depth maps.
no code implementations • 3 Oct 2022 • Huanzhou Zhu, Bo Zhao, Gang Chen, Weifeng Chen, Yijie Chen, Liang Shi, Yaodong Yang, Peter Pietzuch, Lei Chen
Yet, current distributed RL systems tie the definition of RL algorithms to their distributed execution: they hard-code particular distribution strategies and only accelerate specific parts of the computation (e. g. policy network updates) on GPU workers.
1 code implementation • 7 Sep 2022 • Jiaxing Zhang, Ruyi Gan, Junjie Wang, Yuxiang Zhang, Lin Zhang, Ping Yang, Xinyu Gao, Ziwei Wu, Xiaoqun Dong, Junqing He, Jianheng Zhuo, Qi Yang, Yongfeng Huang, Xiayu Li, Yanghan Wu, Junyu Lu, Xinyu Zhu, Weifeng Chen, Ting Han, Kunhao Pan, Rui Wang, Hao Wang, XiaoJun Wu, Zhongshen Zeng, Chongpei Chen
We hope that this project will be the foundation of Chinese cognitive intelligence.
no code implementations • CVPR 2021 • Yihe Tang, Weifeng Chen, Yijun Luo, Yuting Zhang
We propose a semi-supervised approach for contemporary object detectors following the teacher-student dual model framework.
no code implementations • 1 Jan 2021 • Emily Walters, Weifeng Chen, Jia Deng
Recent work has proposed the use of human evaluation for image synthesis models, allowing for a reliable method to evaluate the visual quality of generated images.
no code implementations • CVPR 2020 • Weifeng Chen, Shengyi Qian, David Fan, Noriyuki Kojima, Max Hamilton, Jia Deng
Single-view 3D is the task of recovering 3D properties such as depth and surface normals from a single image.
no code implementations • 20 Aug 2019 • Yu-Wei Chao, Jimei Yang, Weifeng Chen, Jia Deng
We experimentally demonstrate the strength of our approach over different non-hierarchical and hierarchical baselines.
no code implementations • CVPR 2019 • Weifeng Chen, Shengyi Qian, Jia Deng
Depth estimation from a single image in the wild remains a challenging problem.
no code implementations • ICCV 2017 • Weifeng Chen, Donglai Xiang, Jia Deng
We study the problem of single-image depth estimation for images in the wild.
4 code implementations • NeurIPS 2016 • Weifeng Chen, Zhao Fu, Dawei Yang, Jia Deng
This paper studies single-image depth perception in the wild, i. e., recovering depth from a single image taken in unconstrained settings.