SEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020, Oral)
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Updated
Jun 2, 2021 - Python
SEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020, Oral)
[NeurIPS 2024] The official implementation of HairFastGAN. A framework for virtual hairstyle fitting.
[CVPR 2023] Collaborative Diffusion
(CVPR 2023) E4S: Fine-grained Face Swapping via Regional GAN Inversion
Customized fork of Rope Deepfake software featuring live streaming capabilities and support for Deepfacelive models
AttGAN PyTorch Arbitrary Facial Attribute Editing: Only Change What You Want
Various applications based on Stylegan2 Style mixing that can be inference on cpu.
[ECCV 2020] "Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches"
This is the official implementation of "Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors", which is accepted at ICLR2025.
InterfaceGAN++: Exploring the limits of InterfaceGAN. In this repository, we propose an approach, termed as InterFaceGAN++, for semantic face editing based on the work from Shen et al. Specifically, we leverage the ideas from the previous work, by applying the method for new face attributes, and also for StyleGAN3. We qualitatively explain that …
Re-implementation of e4e that use mobilenet-v3 and stylegan2-1024p
a simple pipeline for face editing
Re-implementation of pSp that use mobilenet-v3 and stylegan2-256p
[CVPR 2025] Official Implementation of the Paper "Enhancing Facial Privacy Protection via Weakening Diffusion Purification"
Diverse Facial Edit with StyleGAN, StyleGAN2, StyleClip with ViT, and Other Features like Background Removal and Face Swap
The code includes an iterative editing process for automatic control and instance-based boundary generation, enabling controlled attribute modifications in generated faces.
[ECCV 2020] Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches
A caricature generation model from NIR facial images
The project focuses on manipulating facial features by adjusting the latent vectors within StyleGAN's architecture. Our objective is to achieve precise modifications in age, gender, and facial expressions in generated images.
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