no code implementations • 21 May 2024 • Jinshu Chen, Bingchuan Li, Miao Hua, Panpan Xu, Qian He
Existing solutions to image editing tasks suffer from several issues.
no code implementations • ICCV 2023 • Tianxiang Ma, Bingchuan Li, Qian He, Jing Dong, Tieniu Tan
In this paper, we introduce a novel Geometry-aware Facial Expression Translation (GaFET) framework, which is based on parametric 3D facial representations and can stably decoupled expression.
no code implementations • 3 Feb 2023 • Tianxiang Ma, Bingchuan Li, Wei Liu, Miao Hua, Jing Dong, Tieniu Tan
In this paper, we propose a more general learning approach by considering two domain features as a whole and learning both inter-domain correspondence and intra-domain potential information interactions.
1 code implementation • 3 Feb 2023 • Tianxiang Ma, Bingchuan Li, Qian He, Jing Dong, Tieniu Tan
CNeRF divides the image by semantic regions and learns an independent neural radiance field for each region, and finally fuses them and renders the complete image.
no code implementations • 31 Jan 2023 • Bingchuan Li, Tianxiang Ma, Peng Zhang, Miao Hua, Wei Liu, Qian He, Zili Yi
Specifically, in Phase I, a W-space-oriented StyleGAN inversion network is trained and used to perform image inversion and editing, which assures the editability but sacrifices reconstruction quality.
1 code implementation • 22 Sep 2021 • Miao Hua, Lijie Liu, Ziyang Cheng, Qian He, Bingchuan Li, Zili Yi
Whereas, this technique does not satisfy the requirements of facial parts removal, as it is hard to obtain ``ground-truth'' images with real ``blank'' faces.
1 code implementation • 22 Sep 2021 • Bingchuan Li, Shaofei Cai, Wei Liu, Peng Zhang, Qian He, Miao Hua, Zili Yi
To address these limitations, we design a Dynamic Style Manipulation Network (DyStyle) whose structure and parameters vary by input samples, to perform nonlinear and adaptive manipulation of latent codes for flexible and precise attribute control.