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20222026
most citedXDGAN: Multi-Modal 3D Shape Generation in 2D Space

2 citations · 5 across the 6 of their papers we have counts for

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cs.CV2026

Geo-ID: Test-Time Geometric Consensus for Cross-View Consistent Intrinsics

Alara Dirik, Stefanos Zafeiriou

Intrinsic image decomposition aims to estimate physically based rendering (PBR) parameters such as albedo, roughness, and metallicity from images. While recent methods achieve stro…

cs.CV2025

ReasonX: MLLM-Guided Intrinsic Image Decomposition

Alara Dirik, Tuanfeng Wang, Duygu Ceylan +2

Intrinsic image decomposition aims to separate images into physical components such as albedo, depth, normals, and illumination. While recent diffusion- and transformer-based model…

cs.CV20221 cited

3D-LatentMapper: View Agnostic Single-View Reconstruction of 3D Shapes

Alara Dirik, Pinar Yanardag

Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to represent and generate 3D shapes, as well as a vast number of use cases. However…

cs.CV20222 cited

XDGAN: Multi-Modal 3D Shape Generation in 2D Space

Hassan Abu Alhaija, Alara Dirik, André Knörig +2

Generative models for 2D images has recently seen tremendous progress in quality, resolution and speed as a result of the efficiency of 2D convolutional architectures. However it i…

cs.CV20221 cited

FairStyle: Debiasing StyleGAN2 with Style Channel Manipulations

Cemre Karakas, Alara Dirik, Eylul Yalcinkaya +1

Recent advances in generative adversarial networks have shown that it is possible to generate high-resolution and hyperrealistic images. However, the images produced by GANs are on…

cs.CV20221 cited

Text and Image Guided 3D Avatar Generation and Manipulation

Zehranaz Canfes, M. Furkan Atasoy, Alara Dirik +1

The manipulation of latent space has recently become an interesting topic in the field of generative models. Recent research shows that latent directions can be used to manipulate…