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no code implementations • 8 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.

1 code implementation • 28 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.

Ranked #5 on Monocular Depth Estimation on NYU-Depth V2

1 code implementation • 14 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.

no code implementations • 31 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.

no code implementations • 9 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.

1 code implementation • ICLR 2022 • Peihao Zhu, Rameen Abdal, John Femiani, Peter Wonka

The input to our method is trained GAN that can produce images in domain A and a single reference image I_B from domain B.

no code implementations • ICCV 2021 • Dong Lao, Peihao Zhu, Peter Wonka, Ganesh Sundaramoorthi

We consider the problem of filling in missing spatio-temporal regions of a video.

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.

no code implementations • NeurIPS 2021 • Wamiq Reyaz Para, Shariq Farooq Bhat, Paul Guerrero, Tom Kelly, Niloy Mitra, Leonidas Guibas, Peter Wonka

Sketches can be represented as graphs, with the primitives as nodes and the constraints as edges.

no code implementations • 4 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.

1 code implementation • 2 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.

no code implementations • 4 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.

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

We propose an unsupervised segmentation framework for StyleGAN generated objects.

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.

3 code implementations • 13 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.

4 code implementations • CVPR 2021 • Shariq Farooq Bhat, Ibraheem Alhashim, Peter Wonka

We address the problem of estimating a high quality dense depth map from a single RGB input image.

Ranked #3 on Depth Estimation on NYU-Depth V2

no code implementations • ICCV 2021 • Wamiq Para, Paul Guerrero, Tom Kelly, Leonidas Guibas, Peter Wonka

We generate layouts in three steps.

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.

no code implementations • 25 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.

2 code implementations • 6 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.

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.

no code implementations • 28 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.

no code implementations • CVPR 2021 • Biao Zhang, Peter Wonka

In this paper we propose a new framework for point cloud instance segmentation.

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.

3 code implementations • CVPR 2020 • Rameen Abdal, Yipeng Qin, Peter Wonka

We propose Image2StyleGAN++, a flexible image editing framework with many applications.

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.

3 code implementations • 9 Oct 2019 • Chuhang Zou, Jheng-Wei Su, Chi-Han Peng, Alex Colburn, Qi Shan, Peter Wonka, Hung-Kuo Chu, Derek Hoiem

Recent approaches for predicting layouts from 360 panoramas produce excellent results.

no code implementations • 12 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.

2 code implementations • 1 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.

1 code implementation • 29 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.

2 code implementations • 16 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.

Graphics

5 code implementations • ICCV 2019 • Rameen Abdal, Yipeng Qin, Peter Wonka

We propose an efficient algorithm to embed a given image into the latent space of StyleGAN.

42 code implementations • 31 Dec 2018 • Ibraheem Alhashim, Peter Wonka

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

Ranked #15 on Monocular Depth Estimation on KITTI Eigen split

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**

1 code implementation • CVPR 2019 • Shang-Ta Yang, Fu-En Wang, Chi-Han Peng, Peter Wonka, Min Sun, Hung-Kuo Chu

We present a deep learning framework, called DuLa-Net, to predict Manhattan-world 3D room layouts from a single RGB panorama.

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.

1 code implementation • SIGGRAPH Asia 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.

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.

1 code implementation • 19 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.

Graphics

no code implementations • 9 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.

no code implementations • 23 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.

no code implementations • 8 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.

no code implementations • 12 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.

no code implementations • ICCV 2017 • Liangliang Nan, Peter Wonka

We show that reconstruction from point clouds can be cast as a binary labeling problem.

no code implementations • 27 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.

1 code implementation • 11 Jun 2016 • Mohamed Aly, Guangming Zang, Wolfgang Heidrich, Peter Wonka

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

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.

no code implementations • 5 Oct 2015 • Paul Guerrero, Niloy J. Mitra, Peter Wonka

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

no code implementations • 25 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.

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.

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.

no code implementations • 10 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.

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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