3 citations · 9 across the 7 of their papers we have counts for
7 papers
Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images
Bahri Batuhan Bilecen, Ahmet Berke Gokmen, Aysegul Dundar
3D GAN inversion aims to project a single image into the latent space of a 3D Generative Adversarial Network (GAN), thereby achieving 3D geometry reconstruction. While there exist…
CLIPAway: Harmonizing Focused Embeddings for Removing Objects via Diffusion Models
Yigit Ekin, Ahmet Burak Yildirim, Erdem Eren Caglar +3
Advanced image editing techniques, particularly inpainting, are essential for seamlessly removing unwanted elements while preserving visual integrity. Traditional GAN-based methods…
Diverse Semantic Image Editing with Style Codes
Hakan Sivuk, Aysegul Dundar
Semantic image editing requires inpainting pixels following a semantic map. It is a challenging task since this inpainting requires both harmony with the context and strict complia…
Diverse Inpainting and Editing with GAN Inversion
Ahmet Burak Yildirim, Hamza Pehlivan, Bahri Batuhan Bilecen +1
Recent inversion methods have shown that real images can be inverted into StyleGAN's latent space and numerous edits can be achieved on those images thanks to the semantically rich…
Progressive Learning of 3D Reconstruction Network from 2D GAN Data
Aysegul Dundar, Jun Gao, Andrew Tao +1
This paper presents a method to reconstruct high-quality textured 3D models from single images. Current methods rely on datasets with expensive annotations; multi-view images and t…
Refining 3D Human Texture Estimation from a Single Image
Said Fahri Altindis, Adil Meric, Yusuf Dalva +2
Estimating 3D human texture from a single image is essential in graphics and vision. It requires learning a mapping function from input images of humans with diverse poses into the…