Anurag Ranjan

Ph.D. Student

Perceiving Systems

Office: N3.004
Max-Planck-Ring 4
72076 Tübingen
Germany

http://anuragranj.github.io/

GitHub Repository

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Advisor(s):
Michael Black

My research focuses on Deep Learning for dense estimation problems such as Optical Flow. My broad interests include Computer Vision and Machine Learning. My homepage is located here.

Faces and Expressions

Faces, their shape, and their motion are essential to communication. Consequently, we want a model of the face that can capture the full range of face shapes and expressions. Such a model should be realistic, easy to animate, easy to fit to data, and should support inference about human emotion and speech....

Michael Black Timo Bolkart Anurag Ranjan Soubhik Sanyal Tianye Li Javier Romero Cassidy Laidlaw

Learning Deep Representations of 3D

Much of our work focuses on 3D models of objects and scenes. We would like to take advantage of current deep learning approaches in representing and reasoning about 3D. Unfortunately, the standard 2D convolutional models do not readily extend to 3D because they do not match well current 3D data structures....

Michael Black Gernot Riegler Osman Ulusoy Andreas Geiger Anurag Ranjan Timo Bolkart Soubhik Sanyal

Learning from Synthetic Data

Deep learning has brought rapid progress for many computer vision problems but current methods require large training datasets with annotated ground truth. Human annotators tend to be reasonably efficient for tasks like sparse 2D joint estimation, however annotation for other tasks like dense optical...

Javier Romero Anurag Ranjan Michael Black Jonas Wulff David Hoffmann Dimitris Tzionas Siyu Tang Naureen Mahmood

Learning from Synthetic data

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Javier Romero Anurag Ranjan Michael Black Jonas Wulff David Hoffmann Dimitris Tzionas Siyu Tang Naureen Mahmood Gul Varol Cordelia Schmid

Learning Optical Flow

We view optical flow as the projection of the 3D motion field into the image plane. Until recently, optical flow algorithms were designed by hand and incorporated various heuristics. Deep learning methods provide an opportunity to move away from hand-crafted models but have several limitations. The key one...

Michael Black Andreas Geiger Anurag Ranjan Jonas Wulff Deqing Sun Varun Jampani Laura Sevilla Joel Janai Fatma Güney

Scene Models for Optical Flow

Historically, optical flow methods make generic, spatially homogeneous, assumptions about the spatial structure of the 2D image motion. In reality, optical flow varies across an image depending on object class. Simply put, different objects move differently. For rigid objects, the motion is related to the ...

Michael Black Jonas Wulff Anurag Ranjan Laura Sevilla Fatma Güney Varun Jampani Andreas Geiger Deqing Sun

Unsupervised Learning of Flow with Occlusions

Learning to solve optical flow in an end-to-end fashion from examples is attractive as deep neural networks allow for learning more complex hierarchical flow representations directly from annotated data. However, training such models requires large datasets and obtaining ground truth for real images is challenging as labeling dense ...

Joel Janai Fatma Güney Anurag Ranjan Michael Black Andreas Geiger

5 results (View BibTeX file of all listed publications)

2018

Generating 3D Faces using Convolutional Mesh Autoencoders

Ranjan, A., Bolkart, T., Sanyal, S., Black, M. J.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11207, pages: 725-741, Springer, Cham, September 2018 (inproceedings)

Abstract

Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of a face using linear subspaces or higher-order tensor generalizations. Due to this linearity, they can not capture extreme deformations and non-linear expressions. To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. We introduce mesh sampling operations that enable a hierarchical mesh representation that captures non-linear variations in shape and expression at multiple scales within the model. In a variational setting, our model samples diverse realistic 3D faces from a multivariate Gaussian distribution. Our training data consists of 20,466 meshes of extreme expressions captured over 12 different subjects. Despite limited training data, our trained model outperforms state-of-the-art face models with 50% lower reconstruction error, while using 75% fewer parameters. We also show that, replacing the expression space of an existing state-of-the-art face model with our autoencoder, achieves a lower reconstruction error. Our data, model and code are available at http://coma.is.tue.mpg.de/.

code paper supplementary link (url) DOI Project Page Project Page [BibTex]

2018

Ranjan, A., Bolkart, T., Sanyal, S., Black, M. J. Generating 3D Faces using Convolutional Mesh Autoencoders In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11207, pages: 725-741, Springer, Cham, September 2018 (inproceedings)

code paper supplementary link (url) DOI Project Page Project Page [BibTex]

Learning Human Optical Flow

Ranjan, A., Romero, J., Black, M. J.

In 29th British Machine Vision Conference, September 2018 (inproceedings)

Abstract

The optical flow of humans is well known to be useful for the analysis of human action. Given this, we devise an optical flow algorithm specifically for human motion and show that it is superior to generic flow methods. Designing a method by hand is impractical, so we develop a new training database of image sequences with ground truth optical flow. For this we use a 3D model of the human body and motion capture data to synthesize realistic flow fields. We then train a convolutional neural network to estimate human flow fields from pairs of images. Since many applications in human motion analysis depend on speed, and we anticipate mobile applications, we base our method on SpyNet with several modifications. We demonstrate that our trained network is more accurate than a wide range of top methods on held-out test data and that it generalizes well to real image sequences. When combined with a person detector/tracker, the approach provides a full solution to the problem of 2D human flow estimation. Both the code and the dataset are available for research.

video code pdf link (url) Project Page Project Page [BibTex]

Ranjan, A., Romero, J., Black, M. J. Learning Human Optical Flow In 29th British Machine Vision Conference, September 2018 (inproceedings)

video code pdf link (url) Project Page Project Page [BibTex]

Unsupervised Learning of Multi-Frame Optical Flow with Occlusions

Janai, J., Güney, F., Ranjan, A., Black, M. J., Geiger, A.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11220, pages: 713-731, Springer, Cham, September 2018 (inproceedings)

pdf suppmat DOI Project Page [BibTex]

Janai, J., Güney, F., Ranjan, A., Black, M. J., Geiger, A. Unsupervised Learning of Multi-Frame Optical Flow with Occlusions In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 11220, pages: 713-731, Springer, Cham, September 2018 (inproceedings)

pdf suppmat DOI Project Page [BibTex]

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

Ranjan, A., Jampani, V., Kim, K., Sun, D., Wulff, J., Black, M. J.

May 2018 (article)

Abstract

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. Our key insight is that these four fundamental vision problems are coupled and, consequently, learning to solve them together simplifies the problem because the solutions can reinforce each other by exploiting known geometric constraints. In order to model geometric constraints, we introduce Adversarial Collaboration, a framework that facilitates competition and collaboration between neural networks. We go beyond previous work by exploiting geometry more explicitly and segmenting the scene into static and moving regions. Adversarial Collaboration works much like expectation-maximization but with neural networks that act as adversaries, competing to explain pixels that correspond to static or moving regions, and as collaborators through a moderator that assigns pixels to be either static or independently moving. Our novel method integrates all these problems in a common framework and simultaneously reasons about the segmentation of the scene into moving objects and the static background, the camera motion, depth of the static scene structure, and the optical flow of moving objects. Our model is trained without any supervision and achieves state of the art results amongst unsupervised methods.

pdf link (url) Project Page Project Page [BibTex]

Ranjan, A., Jampani, V., Kim, K., Sun, D., Wulff, J., Black, M. J. Adversarial Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation May 2018 (article)

pdf link (url) Project Page Project Page [BibTex]

2017

Optical Flow Estimation using a Spatial Pyramid Network

Ranjan, A., Black, M.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

Abstract

We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning. This estimates large motions in a coarse-to-fine approach by warping one image of a pair at each pyramid level by the current flow estimate and computing an update to the flow. Instead of the standard minimization of an objective function at each pyramid level, we train one deep network per level to compute the flow update. Unlike the recent FlowNet approach, the networks do not need to deal with large motions; these are dealt with by the pyramid. This has several advantages. First, our Spatial Pyramid Network (SPyNet) is much simpler and 96% smaller than FlowNet in terms of model parameters. This makes it more efficient and appropriate for embedded applications. Second, since the flow at each pyramid level is small (< 1 pixel), a convolutional approach applied to pairs of warped images is appropriate. Third, unlike FlowNet, the learned convolution filters appear similar to classical spatio-temporal filters, giving insight into the method and how to improve it. Our results are more accurate than FlowNet on most standard benchmarks, suggesting a new direction of combining classical flow methods with deep learning.

pdf SupMat project/code [BibTex]

2017

Ranjan, A., Black, M. Optical Flow Estimation using a Spatial Pyramid Network In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, July 2017 (inproceedings)

pdf SupMat project/code [BibTex]

Anurag Ranjan

Faces and Expressions

Learning Deep Representations of 3D

Learning from Synthetic Data

Learning from Synthetic data

Learning Optical Flow

Scene Models for Optical Flow

Unsupervised Learning of Flow with Occlusions

2018

Generating 3D Faces using Convolutional Mesh Autoencoders

2018

Learning Human Optical Flow

Unsupervised Learning of Multi-Frame Optical Flow with Occlusions

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

2017

Optical Flow Estimation using a Spatial Pyramid Network

2017

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