no code implementations • ECCV 2020 • Lingzhi Zhang, Tarmily Wen, Jie Min, Jiancong Wang, David Han, Jianbo Shi
We study the problem of common sense placement of visual objects in an image.
no code implementations • 23 May 2024 • Katherine Xu, Lingzhi Zhang, Jianbo Shi
In this work, we conduct a large-scale scientific study into the impact of random seeds during diffusion inference.
1 code implementation • 28 Mar 2024 • Katherine Xu, Lingzhi Zhang, Jianbo Shi
Modern text-to-image (T2I) diffusion models can generate images with remarkable realism and creativity.
no code implementations • CVPR 2024 • Mang Tik Chiu, Yuqian Zhou, Lingzhi Zhang, Zhe Lin, Connelly Barnes, Sohrab Amirghodsi, Eli Shechtman, Humphrey Shi
Object inpainting is a task that involves adding objects to real images and seamlessly compositing them.
no code implementations • CVPR 2024 • Katherine Xu, Lingzhi Zhang, Jianbo Shi
We propose to sidestep many of the difficulties of existing approaches, which typically involve a two-step process of predicting amodal masks and then generating pixels.
1 code implementation • ICCV 2023 • Lingzhi Zhang, Zhengjie Xu, Connelly Barnes, Yuqian Zhou, Qing Liu, He Zhang, Sohrab Amirghodsi, Zhe Lin, Eli Shechtman, Jianbo Shi
Recent advancements in deep generative models have facilitated the creation of photo-realistic images across various tasks.
1 code implementation • 7 Aug 2022 • Lingzhi Zhang, Shenghao Zhou, Simon Stent, Jianbo Shi
Egocentric videos offer fine-grained information for high-fidelity modeling of human behaviors.
no code implementations • 6 Aug 2022 • Lingzhi Zhang, Connelly Barnes, Kevin Wampler, Sohrab Amirghodsi, Eli Shechtman, Zhe Lin, Jianbo Shi
Recently, deep models have established SOTA performance for low-resolution image inpainting, but they lack fidelity at resolutions associated with modern cameras such as 4K or more, and for large holes.
1 code implementation • 5 Aug 2022 • Lingzhi Zhang, Yuqian Zhou, Connelly Barnes, Sohrab Amirghodsi, Zhe Lin, Eli Shechtman, Jianbo Shi
Inspired by this workflow, we propose a new learning task of automatic segmentation of inpainting perceptual artifacts, and apply the model for inpainting model evaluation and iterative refinement.
no code implementations • 3 Jun 2020 • Lingzhi Zhang, Jiancong Wang, Yinshuang Xu, Jie Min, Tarmily Wen, James C. Gee, Jianbo Shi
We propose an image synthesis approach that provides stratified navigation in the latent code space.
no code implementations • CVPR 2020 • Lingzhi Zhang, Jiancong Wang, Yinshuang Xu, Jie Min, Tarmily Wen, James C. Gee, Jianbo Shi
We propose an image synthesis approach that provides stratified navigation in the latent code space.
1 code implementation • 25 Oct 2019 • Lingzhi Zhang, Jiancong Wang, Jianbo Shi
In this paper, we study the problem of generating a set ofrealistic and diverse backgrounds when given only a smallforeground region.
2 code implementations • 25 Oct 2019 • Lingzhi Zhang, Tarmily Wen, Jianbo Shi
In addition, we jointly optimize the proposed Poisson blending loss as well as the style and content loss computed from a deep network, and reconstruct the blending region by iteratively updating the pixels using the L-BFGS solver.
no code implementations • 13 Jul 2019 • Lingzhi Zhang, Andong Cao, Rui Li, Jianbo Shi
In common real-world robotic operations, action and state spaces can be vast and sometimes unknown, and observations are often relatively sparse.