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Found 8 papers, 2 papers with code
RS-Corrector: Correcting the Racial Stereotypes in Latent Diffusion Models
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.
GaFET: Learning Geometry-aware Facial Expression Translation from In-The-Wild Images
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.
Freestyle 3D-Aware Portrait Synthesis Based on Compositional Generative Priors
Efficiently generating a freestyle 3D portrait with high quality and 3D-consistency is a promising yet challenging task.
CFFT-GAN: Cross-domain Feature Fusion Transformer for Exemplar-based Image Translation
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.
Semantic 3D-aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field
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.
ReGANIE: Rectifying GAN Inversion Errors for Accurate Real Image Editing
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.
CFA-Net: Controllable Face Anonymization Network with Identity Representation Manipulation
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.
MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation
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.
