Search Results for author: Peter Wonka

Found 53 papers, 23 papers with code

Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization

no code implementations8 May 2022 Ngan Nguyen, Feng Liang, Dominik Engel, Ciril Bohak, Peter Wonka, Timo Ropinski, Ivan Viola

We propose a new microscopy simulation system that can depict atomistic models in a micrograph visual style, similar to results of physical electron microscopy imaging.


LocalBins: Improving Depth Estimation by Learning Local Distributions

1 code implementation28 Mar 2022 Shariq Farooq Bhat, Ibraheem Alhashim, Peter Wonka

We build on AdaBins which estimates a global distribution of depth values for the input image and evolve the architecture in two ways.

Monocular Depth Estimation

InsetGAN for Full-Body Image Generation

1 code implementation14 Mar 2022 Anna Frühstück, Krishna Kumar Singh, Eli Shechtman, Niloy J. Mitra, Peter Wonka, Jingwan Lu

Instead of modeling this complex domain with a single GAN, we propose a novel method to combine multiple pretrained GANs, where one GAN generates a global canvas (e. g., human body) and a set of specialized GANs, or insets, focus on different parts (e. g., faces, shoes) that can be seamlessly inserted onto the global canvas.

Image Generation

On the Robustness of Quality Measures for GANs

no code implementations31 Jan 2022 Motasem Alfarra, Juan C. Pérez, Anna Frühstück, Philip H. S. Torr, Peter Wonka, Bernard Ghanem

We show the vulnerability of both the generative model and the FID against additive perturbations in the latent space.

CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directions

no code implementations9 Dec 2021 Rameen Abdal, Peihao Zhu, John Femiani, Niloy J. Mitra, Peter Wonka

The success of StyleGAN has enabled unprecedented semantic editing capabilities, on both synthesized and real images.

Zero-Shot Learning

Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence With Functional Maps

no code implementations CVPR 2021 Gautam Pai, Jing Ren, Simone Melzi, Peter Wonka, Maks Ovsjanikov

In this paper, we provide a theoretical foundation for pointwise map recovery from functional maps and highlight its relation to a range of shape correspondence methods based on spectral alignment.

Self-Supervised Learning of Domain Invariant Features for Depth Estimation

no code implementations4 Jun 2021 Hiroyasu Akada, Shariq Farooq Bhat, Ibraheem Alhashim, Peter Wonka

Specifically, we extend self-supervised learning from traditional representation learning, which works on images from a single domain, to domain invariant representation learning, which works on images from two different domains by utilizing an image-to-image translation network.

Depth Estimation Domain Adaptation +5

Barbershop: GAN-based Image Compositing using Segmentation Masks

1 code implementation2 Jun 2021 Peihao Zhu, Rameen Abdal, John Femiani, Peter Wonka

Seamlessly blending features from multiple images is extremely challenging because of complex relationships in lighting, geometry, and partial occlusion which cause coupling between different parts of the image.

Finding Nano-Ötzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography

no code implementations4 Apr 2021 Ngan Nguyen, Ciril Bohak, Dominik Engel, Peter Mindek, Ondřej Strnad, Peter Wonka, Sai Li, Timo Ropinski, Ivan Viola

Our technique shows the high impact in target sciences for visual data analysis of very noisy volumes that cannot be visualized with existing techniques.

Electron Tomography

Labels4Free: Unsupervised Segmentation using StyleGAN

1 code implementation ICCV 2021 Rameen Abdal, Peihao Zhu, Niloy Mitra, Peter Wonka

We propose an unsupervised segmentation framework for StyleGAN generated objects.

IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and Prediction

1 code implementation ICCV 2021 Guangming Zang, Ramzi Idoughi, Rui Li, Peter Wonka, Wolfgang Heidrich

After getting estimated through the sinogram prediction module, the density field is consistently refined in the second module using local and non-local geometrical priors.

Computed Tomography (CT) Self-Supervised Learning +1

Improved StyleGAN Embedding: Where are the Good Latents?

3 code implementations13 Dec 2020 Peihao Zhu, Rameen Abdal, Yipeng Qin, John Femiani, Peter Wonka

First, we introduce a new normalized space to analyze the diversity and the quality of the reconstructed latent codes.

Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization

no code implementations ICLR 2022 Biao Zhang, Peter Wonka

We combine training data generating networks with bi-level optimization algorithms to obtain a complete framework for which all components can be jointly trained.

3D Shape Reconstruction 3D Shape Representation +2

Channel-Directed Gradients for Optimization of Convolutional Neural Networks

no code implementations25 Aug 2020 Dong Lao, Peihao Zhu, Peter Wonka, Ganesh Sundaramoorthi

We introduce optimization methods for convolutional neural networks that can be used to improve existing gradient-based optimization in terms of generalization error.

StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows

2 code implementations6 Aug 2020 Rameen Abdal, Peihao Zhu, Niloy Mitra, Peter Wonka

We evaluate our method using the face and the car latent space of StyleGAN, and demonstrate fine-grained disentangled edits along various attributes on both real photographs and StyleGAN generated images.

Disentangled Image Generation Through Structured Noise Injection

1 code implementation CVPR 2020 Yazeed Alharbi, Peter Wonka

We show that disentanglement in the first layer of the generator network leads to disentanglement in the generated image.

Disentanglement Image Generation

MGCN: Descriptor Learning using Multiscale GCNs

no code implementations28 Jan 2020 Yiqun Wang, Jing Ren, Dong-Ming Yan, Jianwei Guo, Xiaopeng Zhang, Peter Wonka

Second, we propose a new multiscale graph convolutional network (MGCN) to transform a non-learned feature to a more discriminative descriptor.

SEAN: Image Synthesis with Semantic Region-Adaptive Normalization

1 code implementation CVPR 2020 Peihao Zhu, Rameen Abdal, Yipeng Qin, Peter Wonka

Using SEAN normalization, we can build a network architecture that can control the style of each semantic region individually, e. g., we can specify one style reference image per region.

Image Generation

StructEdit: Learning Structural Shape Variations

1 code implementation CVPR 2020 Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, Leonidas J. Guibas

Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape, transferring edits from one shape to another, and many other applications in 3D content creation.

Photorealistic Material Editing Through Direct Image Manipulation

no code implementations12 Sep 2019 Károly Zsolnai-Fehér, Peter Wonka, Michael Wimmer

In the proposed workflow, the user starts with an input image and applies a few intuitive transforms (e. g., colorization, image inpainting) within a 2D image editor of their choice, and in the next step, our technique produces a photorealistic result that approximates this target image.

Colorization Image Inpainting +1

StructureNet: Hierarchical Graph Networks for 3D Shape Generation

2 code implementations1 Aug 2019 Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, Leonidas J. Guibas

We introduce StructureNet, a hierarchical graph network which (i) can directly encode shapes represented as such n-ary graphs; (ii) can be robustly trained on large and complex shape families; and (iii) can be used to generate a great diversity of realistic structured shape geometries.

3D Shape Generation

TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures

1 code implementation29 Apr 2019 Anna Frühstück, Ibraheem Alhashim, Peter Wonka

We tackle the problem of texture synthesis in the setting where many input images are given and a large-scale output is required.

Image Generation Image Stylization +1

ZoomOut: Spectral Upsampling for Efficient Shape Correspondence

2 code implementations16 Apr 2019 Simone Melzi, Jing Ren, Emanuele Rodolà, Abhishek Sharma, Peter Wonka, Maks Ovsjanikov

Our main observation is that high quality maps can be obtained even if the input correspondences are noisy or are encoded by a small number of coefficients in a spectral basis.


High Quality Monocular Depth Estimation via Transfer Learning

42 code implementations31 Dec 2018 Ibraheem Alhashim, Peter Wonka

Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction.

Action Classification Monocular Depth Estimation +1

Latent Filter Scaling for Multimodal Unsupervised Image-to-Image Translation

no code implementations CVPR 2019 Yazeed Alharbi, Neil Smith, Peter Wonka

In multimodal unsupervised image-to-image translation tasks, the goal is to translate an image from the source domain to many images in the target domain.

Disentanglement Multimodal Unsupervised Image-To-Image Translation +2

How does Lipschitz Regularization Influence GAN Training?

no code implementations ECCV 2020 Yipeng Qin, Niloy Mitra, Peter Wonka

In this work, we uncover an even more important effect of Lipschitz regularization by examining its impact on the loss function: It degenerates GAN loss functions to almost linear ones by restricting their domain and interval of attainable gradient values.

Super-Resolution and Sparse View CT Reconstruction

no code implementations ECCV 2018 Guangming Zang, Mohamed Aly, Ramzi Idoughi, Peter Wonka, Wolfgang Heidrich

As a second, smaller contribution, we also show that when using such a proximal reconstruction framework, it is beneficial to employ the Simultaneous Algebraic Reconstruction Technique (SART) instead of the commonly used Conjugate Gradient (CG) method in the solution of the data term proximal operator.

Computed Tomography (CT) Super-Resolution

FrankenGAN: Guided Detail Synthesis for Building Mass-Models Using Style-Synchonized GANs

1 code implementation19 Jun 2018 Tom Kelly, Paul Guerrero, Anthony Steed, Peter Wonka, Niloy J. Mitra

The various GANs are synchronized to produce consistent style distributions over buildings and neighborhoods.


Facade Segmentation in the Wild

no code implementations9 May 2018 John Femiani, Wamiq Reyaz Para, Niloy Mitra, Peter Wonka

Specifically, we propose a MULTIFACSEGNET architecture to assign multiple labels to each pixel, a SEPARABLE architecture as a low-rank formulation that encourages extraction of rectangular elements, and a COMPATIBILITY network that simultaneously seeks segmentation across facade element types allowing the network to 'see' intermediate output probabilities of the various facade element classes.

Semantic Segmentation

Gaussian Material Synthesis

no code implementations23 Apr 2018 Károly Zsolnai-Fehér, Peter Wonka, Michael Wimmer

Workflow timings against Disney's "principled" shader reveal that our system scales well with the number of sought materials, thus empowering even novice users to generate hundreds of high-quality material models without any expertise in material modeling.

Supervised Convolutional Sparse Coding

no code implementations8 Apr 2018 Lama Affara, Bernard Ghanem, Peter Wonka

Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks.

Image Reconstruction Image Restoration

Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline

no code implementations12 Feb 2018 Silvio Giancola, Jens Schneider, Peter Wonka, Bernard S. Ghanem

We also present a technique to filter the pairs of 3D matched points based on the distribution of their distances.

3D Reconstruction

Fast Convolutional Sparse Coding in the Dual Domain

no code implementations27 Sep 2017 Lama Affara, Bernard Ghanem, Peter Wonka

Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning.

Video Compression

TRex: A Tomography Reconstruction Proximal Framework for Robust Sparse View X-Ray Applications

1 code implementation11 Jun 2016 Mohamed Aly, Guangming Zang, Wolfgang Heidrich, Peter Wonka

We present TRex, a flexible and robust Tomographic Reconstruction framework using proximal algorithms.

Intrinsic Scene Decomposition From RGB-D images

no code implementations ICCV 2015 Mohammed Hachama, Bernard Ghanem, Peter Wonka

In this paper, we address the problem of computing an intrinsic decomposition of the colors of a surface into an albedo and a shading term.

Intrinsic Image Decomposition

RAID: A Relation-Augmented Image Descriptor

no code implementations5 Oct 2015 Paul Guerrero, Niloy J. Mitra, Peter Wonka

As humans, we regularly interpret images based on the relations between image regions.

Scaling SVM and Least Absolute Deviations via Exact Data Reduction

no code implementations25 Oct 2013 Jie Wang, Peter Wonka, Jieping Ye

Some appealing features of our screening method are: (1) DVI is safe in the sense that the vectors discarded by DVI are guaranteed to be non-support vectors; (2) the data set needs to be scanned only once to run the screening, whose computational cost is negligible compared to that of solving the SVM problem; (3) DVI is independent of the solvers and can be integrated with any existing efficient solvers.

A Safe Screening Rule for Sparse Logistic Regression

no code implementations NeurIPS 2014 Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, Jieping Ye

The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection.

Lasso Screening Rules via Dual Polytope Projection

no code implementations NeurIPS 2013 Jie Wang, Peter Wonka, Jieping Ye

To improve the efficiency of solving large-scale Lasso problems, El Ghaoui and his colleagues have proposed the SAFE rules which are able to quickly identify the inactive predictors, i. e., predictors that have $0$ components in the solution vector.

Fused Multiple Graphical Lasso

no code implementations10 Sep 2012 Sen Yang, Zhaosong Lu, Xiaotong Shen, Peter Wonka, Jieping Ye

We expect the two brain networks for NC and MCI to share common structures but not to be identical to each other; similarly for the two brain networks for MCI and AD.

Multi-Stage Dantzig Selector

no code implementations NeurIPS 2010 Ji Liu, Peter Wonka, Jieping Ye

We show that if $X$ obeys a certain condition, then with a large probability the difference between the solution $\hat\beta$ estimated by the proposed method and the true solution $\beta^*$ measured in terms of the $l_p$ norm ($p\geq 1$) is bounded as \begin{equation*} \|\hat\beta-\beta^*\|_p\leq \left(C(s-N)^{1/p}\sqrt{\log m}+\Delta\right)\sigma, \end{equation*} $C$ is a constant, $s$ is the number of nonzero entries in $\beta^*$, $\Delta$ is independent of $m$ and is much smaller than the first term, and $N$ is the number of entries of $\beta^*$ larger than a certain value in the order of $\mathcal{O}(\sigma\sqrt{\log m})$.

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