2 citations · 5 across the 8 of their papers we have counts for
9 papers
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…
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…
PRISM: A Unified Framework for Photorealistic Reconstruction and Intrinsic Scene Modeling
Alara Dirik, Tuanfeng Wang, Duygu Ceylan +2
We present PRISM, a unified framework that enables multiple image generation and editing tasks in a single foundational model. Starting from a pre-trained text-to-image diffusion m…
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…
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…
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…
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…
StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation
Umut Kocasari, Alara Dirik, Mert Tiftikci +1
Discovering meaningful directions in the latent space of GANs to manipulate semantic attributes typically requires large amounts of labeled data. Recent work aims to overcome this…
Controlled Cue Generation for Play Scripts
Alara Dirik, Hilal Donmez, Pinar Yanardag
In this paper, we use a large-scale play scripts dataset to propose the novel task of theatrical cue generation from dialogues. Using over one million lines of dialogue and cues, w…