Search Results for author: Jonas Wulff

Found 11 papers, 3 papers with code

Learning to See by Looking at Noise

1 code implementation10 Jun 2021 Manel Baradad, Jonas Wulff, Tongzhou Wang, Phillip Isola, Antonio Torralba

We investigate a suite of image generation models that produce images from simple random processes.

Image Generation

Using latent space regression to analyze and leverage compositionality in GANs

no code implementations ICLR 2021 Lucy Chai, Jonas Wulff, Phillip Isola

In this work, we investigate regression into the latent space as a probe to understand the compositional properties of GANs.

GAN inversion Image Inpainting

Improving Inversion and Generation Diversity in StyleGAN using a Gaussianized Latent Space

no code implementations14 Sep 2020 Jonas Wulff, Antonio Torralba

We show that, under a simple nonlinear operation, the data distribution can be modeled as Gaussian and therefore expressed using sufficient statistics.

Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

1 code implementation CVPR 2019 Anurag Ranjan, Varun Jampani, Lukas Balles, Kihwan Kim, Deqing Sun, Jonas Wulff, Michael J. Black

We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions.

Monocular Depth Estimation Motion Estimation +2

Optical Flow in Mostly Rigid Scenes

no code implementations CVPR 2017 Jonas Wulff, Laura Sevilla-Lara, Michael J. Black

Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes.

Motion Estimation Optical Flow Estimation

Efficient Sparse-to-Dense Optical Flow Estimation Using a Learned Basis and Layers

no code implementations CVPR 2015 Jonas Wulff, Michael J. Black

Given a set of sparse matches, we regress to dense optical flow using a learned set of full-frame basis flow fields.

Optical Flow Estimation

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