2 code implementations • 11 Jun 2017 • Lvmin Zhang, Yi Ji, Xin Lin
Recently, with the revolutionary neural style transferring methods, creditable paintings can be synthesized automatically from content images and style images.
no code implementations • ICCV 2021 • Lvmin Zhang, Jinyue Jiang, Yi Ji, Chunping Liu
SmartShadow is a deep learning application for digital painting artists to draw shadows on line drawings, with three proposed tools.
no code implementations • CVPR 2021 • Lvmin Zhang, Chengze Li, Edgar Simo-Serra, Yi Ji, Tien-Tsin Wong, Chunping Liu
We present a deep learning framework for user-guided line art flat filling that can compute the "influence areas" of the user color scribbles, i. e., the areas where the user scribbles should propagate and influence.
no code implementations • CVPR 2021 • Lvmin Zhang, Xinrui Wang, Qingnan Fan, Yi Ji, Chunping Liu
To this end, we create a large-scale dataset with these three components annotated by artists in a human-in-the-loop manner.
4 code implementations • ICCV 2023 • Lvmin Zhang, Anyi Rao, Maneesh Agrawala
ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls.
3 code implementations • 27 Feb 2024 • Lvmin Zhang, Maneesh Agrawala
We show that latent transparency can be applied to different open source image generators, or be adapted to various conditional control systems to achieve applications like foreground/background-conditioned layer generation, joint layer generation, structural control of layer contents, etc.
no code implementations • ECCV 2020 • Lvmin Zhang, Chengze Li, Yi Ji, Chunping Liu, Tien-Tsin Wong
We show that partially ""erasing"" the appearance preservation facilitate adequate image smoothing.
1 code implementation • ECCV 2020 • Lvmin Zhang, Yi JI, Chunping Liu
We detail the challenges in achieving this dataset and present a human-in-the-loop workflow namely Feasibility-based Assignment Recommendation (FAR) to enable large-scale annotating.