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Found 7 papers, 2 papers with code
GANTASTIC: GAN-based Transfer of Interpretable Directions for Disentangled Image Editing in Text-to-Image Diffusion Models
The rapid advancement in image generation models has predominantly been driven by diffusion models, which have demonstrated unparalleled success in generating high-fidelity, diverse images from textual prompts.
NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models
Our extensive experiments show that our method achieves highly disentangled edits, outperforming existing approaches in both diffusion-based and GAN-based latent space editing methods.
Refining 3D Human Texture Estimation from a Single Image
Estimating 3D human texture from a single image is essential in graphics and vision.
Face Attribute Editing with Disentangled Latent Vectors
For this goal, inspired by the latent space factorization works of fixed pretrained GANs, we design the attribute editing by latent space factorization, and for each attribute, we learn a linear direction that is orthogonal to the others.
StyleRes: Transforming the Residuals for Real Image Editing with StyleGAN
We present a novel image inversion framework and a training pipeline to achieve high-fidelity image inversion with high-quality attribute editing.
VecGAN: Image-to-Image Translation with Interpretable Latent Directions
However, while those models cannot be trained end-to-end and struggle to edit encoded images precisely, VecGAN is end-to-end trained for image translation task and successful at editing an attribute while preserving the others.
Benchmarking the Robustness of Instance Segmentation Models
These presented robustness and generalization evaluations are important when designing instance segmentation models for real-world applications and picking an off-the-shelf pretrained model to directly use for the task at hand.
