publications

2024

1. 

   Physically Based Neural Bidirectional Reflectance Distribution Function

   Chenliang Zhou, Alejandro Sztrajman, Rainer Gilles, and 6 more authors

   Under Review, 2024
2. NeurIPS-WiML

   

   Text-guided Transient Attribute Transfer

   Fazilet Gokbudak, and Cengiz Oztireli

   NeurIPS’24 Women in Machine Learning Workshop, 2024
3. ECCV

   

   Hypernetworks for Generalizable BRDF Representation

   Fazilet Gokbudak, Alejandro Sztrajman, Chenliang Zhou, and 3 more authors

   The 18th European Conference on Computer Vision ECCV, 2024

   Abs PDF Code Website

   In this paper, we introduce a technique to estimate measured BRDFs from a sparse set of samples. Our approach offers accurate BRDF reconstructions that are generalizable to new materials. This opens the door to BDRF reconstructions from a variety of data sources. The success of our approach relies on the ability of hypernetworks to generate a robust representation of BRDFs and a set encoder that allows us to feed inputs of different sizes to the architecture. The set encoder and the hypernetwork also enable the compression of densely sampled BRDFs. We evaluate our technique both qualitatively and quantitatively on the well-known MERL dataset of 100 isotropic materials. Our approach accurately 1) estimates the BRDFs of unseen materials even for an extremely sparse sampling, 2) compresses the measured BRDFs into very small embeddings, e.g., 7D.

4. 

   Multispectral Fine-Grained Classification of Blackgrass in Wheat and Barley Crops

   Madeleine Darbyshire, Shaun Coutts, Eleanor Hammond, and 6 more authors

   2024

   arXiv

2023

1. CVMP

   

   One-shot Detail Retouching with Patch Space Neural Transformation Blending

   Fazilet Gokbudak, and A. Cengiz Oztireli

   In Proceedings of the 20th ACM SIGGRAPH European Conference on Visual Media Production, London, United Kingdom, 2023

   Abs PDF Supp Code Website

   Photo retouching is a difficult task for novice users as it requires expert knowledge and advanced tools. Photographers often spend a great deal of time generating high-quality retouched photos with intricate details. In this paper, we introduce a one-shot learning based technique to automatically retouch details of an input image based on just a single pair of before and after example images. Our approach provides accurate and generalizable detail edit transfer to new images. We achieve these by proposing a new representation for image to image maps. Specifically, we propose neural field based transformation blending in the patch space for defining patch to patch transformations for each frequency band. This parametrization of the map with anchor transformations and associated weights, and spatio-spectral localized patches, allows us to capture details well while staying generalizable. We evaluate our technique both on known ground truth filters and artist retouching edits. Our method accurately transfers complex detail retouching edits.

2019

1. 

   Spatial Receptor Allocation for a Multiple Access Hub in Nanonetworks

   Fazilet Gokbudak, Bayram Cevdet Akdeniz, Tuna Tugcu, and 1 more author

   IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, 2019

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