no code implementations • 8 Dec 2023 • Yue Jiang, Yueming Lyu, Tianxiang Ma, Bo Peng, Jing Dong
Extensive empirical evaluations demonstrate that the introduced \themodel effectively corrects the racial stereotypes of the well-trained Stable Diffusion model while leaving the original model unchanged.
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 • 27 Jun 2023 • Tianxiang Ma, Kang Zhao, Jianxin Sun, Yingya Zhang, Jing Dong
Efficiently generating a freestyle 3D portrait with high quality and 3D-consistency is a promising yet challenging task.
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.
no code implementations • 24 May 2021 • Tianxiang Ma, Dongze Li, Wei Wang, Jing Dong
We propose a Controllable Face Anonymization Network (CFA-Net), a novel approach that can anonymize the identity of given faces in images and videos, based on a generator that can disentangle face identity from other image contents.
1 code implementation • CVPR 2021 • Tianxiang Ma, Bo Peng, Wei Wang, Jing Dong
To deal with this problem, we propose a novel multi-level statistics transfer model, which disentangles and transfers multi-level appearance features from person images and merges them with pose features to reconstruct the source person images themselves.