no code implementations • 27 May 2024 • Fangneng Zhan, Hanxue Liang, Yifan Wang, Michael Niemeyer, Michael Oechsle, Adam Kortylewski, Cengiz Oztireli, Gordon Wetzstein, Christian Theobalt
Central to this framework is the development of differentiable versions of these rendering elements, allowing for effective gradient backpropagation from the final rendering objectives.
no code implementations • 20 May 2024 • Tianhao Wu, Jing Yang, Zhilin Guo, Jingyi Wan, Fangcheng Zhong, Cengiz Oztireli
By equipping the most recent 3D Gaussian Splatting representation with head 3D morphable models (3DMM), existing methods manage to create head avatars with high fidelity.
no code implementations • 3 May 2024 • Madeleine Darbyshire, Shaun Coutts, Eleanor Hammond, Fazilet Gokbudak, Cengiz Oztireli, Petra Bosilj, Junfeng Gao, Elizabeth Sklar, Simon Parsons
This is particularly true for cereal crops, like wheat and barley, that are staple food crops and occupy a globally significant portion of agricultural land.
no code implementations • 21 Nov 2023 • Andrew Spielberg, Fangcheng Zhong, Konstantinos Rematas, Krishna Murthy Jatavallabhula, Cengiz Oztireli, Tzu-Mao Li, Derek Nowrouzezahrai
This approach is predicated by neural network differentiability, the requirement that analytic derivatives of a given problem's task metric can be computed with respect to neural network's parameters.
no code implementations • 20 Nov 2023 • Chenliang Zhou, Fangcheng Zhong, Param Hanji, Zhilin Guo, Kyle Fogarty, Alejandro Sztrajman, Hongyun Gao, Cengiz Oztireli
We propose FrePolad: frequency-rectified point latent diffusion, a point cloud generation pipeline integrating a variational autoencoder (VAE) with a denoising diffusion probabilistic model (DDPM) for the latent distribution.
no code implementations • 23 Aug 2023 • Wenzhao Li, Tianhao Wu, Fangcheng Zhong, Cengiz Oztireli
We highlight a research gap in radiance fields style transfer, the lack of sufficient perceptual controllability, motivated by the existing concept in the 2D image style transfer.
no code implementations • 24 Mar 2023 • Hanxue Liang, Tianhao Wu, Param Hanji, Francesco Banterle, Hongyun Gao, Rafal Mantiuk, Cengiz Oztireli
We measured the quality of videos synthesized by several NVS methods in a well-controlled perceptual quality assessment experiment as well as with many existing state-of-the-art image/video quality metrics.
no code implementations • 17 Mar 2023 • Tianhao Wu, Hanxue Liang, Fangcheng Zhong, Gernot Riegler, Shimon Vainer, Cengiz Oztireli
While neural radiance field (NeRF) based methods can model semi-transparency and achieve photo-realistic quality in synthesized novel views, their volumetric geometry representation tightly couples geometry and opacity, and therefore cannot be easily converted into surfaces without introducing artifacts.
no code implementations • CVPR 2023 • Fei Yin, Yong Zhang, Xuan Wang, Tengfei Wang, Xiaoyu Li, Yuan Gong, Yanbo Fan, Xiaodong Cun, Ying Shan, Cengiz Oztireli, Yujiu Yang
It is natural to associate 3D GANs with GAN inversion methods to project a real image into the generator's latent space, allowing free-view consistent synthesis and editing, referred as 3D GAN inversion.
no code implementations • CVPR 2023 • Cristina Vasconcelos, Cengiz Oztireli, Mark Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi
Neural fields have rapidly been adopted for representing 3D signals, but their application to more classical 2D image-processing has been relatively limited.
no code implementations • 8 Oct 2022 • Chenliang Zhou, Fangcheng Zhong, Cengiz Oztireli
We introduce CLIP Projection-Augmentation Embedding (PAE) as an optimization target to improve the performance of text-guided image manipulation.
1 code implementation • 3 Oct 2022 • Fazilet Gokbudak, Cengiz Oztireli
Our method accurately transfers complex detail retouching edits.
no code implementations • 31 May 2022 • Tianhao Wu, Fangcheng Zhong, Andrea Tagliasacchi, Forrester Cole, Cengiz Oztireli
We introduce Decoupled Dynamic Neural Radiance Field (D$^2$NeRF), a self-supervised approach that takes a monocular video and learns a 3D scene representation which decouples moving objects, including their shadows, from the static background.
1 code implementation • CVPR 2022 • Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam Laradji, Hsueh-Ti, Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, Andrea Tagliasacchi
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
no code implementations • CVPR 2021 • Wang Yifan, Shihao Wu, Cengiz Oztireli, Olga Sorkine-Hornung
Neural implicit functions have emerged as a powerful representation for surfaces in 3D.
no code implementations • CVPR 2018 • Riccardo Roveri, Lukas Rahmann, Cengiz Oztireli, Markus Gross
We propose a novel neural network architecture for point cloud classification.
no code implementations • CVPR 2017 • Endri Dibra, Himanshu Jain, Cengiz Oztireli, Remo Ziegler, Markus Gross
In this work, we present a novel method for capturing human body shape from a single scaled silhouette.