Search Results for author: Umar Iqbal

Found 25 papers, 10 papers with code

DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars

no code implementations29 Mar 2022 Amit Raj, Umar Iqbal, Koki Nagano, Sameh Khamis, Pavlo Molchanov, James Hays, Jan Kautz

In this work, we present, DRaCoN, a framework for learning full-body volumetric avatars which exploits the advantages of both the 2D and 3D neural rendering techniques.

Neural Rendering

Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects

1 code implementation CVPR 2022 Atsuhiro Noguchi, Umar Iqbal, Jonathan Tremblay, Tatsuya Harada, Orazio Gallo

Rendering articulated objects while controlling their poses is critical to applications such as virtual reality or animation for movies.

GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras

1 code implementation CVPR 2022 Ye Yuan, Umar Iqbal, Pavlo Molchanov, Kris Kitani, Jan Kautz

Since the joint reconstruction of human motions and camera poses is underconstrained, we propose a global trajectory predictor that generates global human trajectories based on local body movements.

3D Human Pose Estimation Human Mesh Recovery

Physics-based Human Motion Estimation and Synthesis from Videos

no code implementations ICCV 2021 Kevin Xie, Tingwu Wang, Umar Iqbal, Yunrong Guo, Sanja Fidler, Florian Shkurti

We demonstrate both qualitatively and quantitatively significantly improved motion estimation, synthesis quality and physical plausibility achieved by our method on the large scale Human3. 6m dataset \cite{h36m_pami} as compared to prior kinematic and physics-based methods.

Motion Estimation motion synthesis +1

Adversarial Motion Modelling helps Semi-supervised Hand Pose Estimation

no code implementations10 Jun 2021 Adrian Spurr, Pavlo Molchanov, Umar Iqbal, Jan Kautz, Otmar Hilliges

Hand pose estimation is difficult due to different environmental conditions, object- and self-occlusion as well as diversity in hand shape and appearance.

Hand Pose Estimation

Weakly-Supervised Physically Unconstrained Gaze Estimation

1 code implementation CVPR 2021 Rakshit Kothari, Shalini De Mello, Umar Iqbal, Wonmin Byeon, Seonwook Park, Jan Kautz

A major challenge for physically unconstrained gaze estimation is acquiring training data with 3D gaze annotations for in-the-wild and outdoor scenarios.

Domain Generalization Gaze Estimation

KAMA: 3D Keypoint Aware Body Mesh Articulation

no code implementations27 Apr 2021 Umar Iqbal, Kevin Xie, Yunrong Guo, Jan Kautz, Pavlo Molchanov

We present KAMA, a 3D Keypoint Aware Mesh Articulation approach that allows us to estimate a human body mesh from the positions of 3D body keypoints.

3D Human Pose Estimation 3D Human Shape Estimation +1

Learning to Track Instances without Video Annotations

no code implementations CVPR 2021 Yang Fu, Sifei Liu, Umar Iqbal, Shalini De Mello, Humphrey Shi, Jan Kautz

Tracking segmentation masks of multiple instances has been intensively studied, but still faces two fundamental challenges: 1) the requirement of large-scale, frame-wise annotation, and 2) the complexity of two-stage approaches.

Instance Segmentation Pose Estimation +1

A4 : Evading Learning-based Adblockers

no code implementations29 Jan 2020 Shitong Zhu, Zhongjie Wang, Xun Chen, Shasha Li, Umar Iqbal, Zhiyun Qian, Kevin S. Chan, Srikanth V. Krishnamurthy, Zubair Shafiq

Efforts by online ad publishers to circumvent traditional ad blockers towards regaining fiduciary benefits, have been demonstrably successful.

Few-Shot Adaptive Gaze Estimation

1 code implementation ICCV 2019 Seonwook Park, Shalini De Mello, Pavlo Molchanov, Umar Iqbal, Otmar Hilliges, Jan Kautz

Inter-personal anatomical differences limit the accuracy of person-independent gaze estimation networks.

 Ranked #1 on Gaze Estimation on MPII Gaze (using extra training data)

Gaze Estimation Meta-Learning

AdGraph: A Graph-Based Approach to Ad and Tracker Blocking

1 code implementation22 May 2018 Umar Iqbal, Peter Snyder, Shitong Zhu, Benjamin Livshits, Zhiyun Qian, Zubair Shafiq

AdGraph differs from existing approaches by building a graph representation of the HTML structure, network requests, and JavaScript behavior of a webpage, and using this unique representation to train a classifier for identifying advertising and tracking resources.

Joint Flow: Temporal Flow Fields for Multi Person Tracking

no code implementations11 May 2018 Andreas Doering, Umar Iqbal, Juergen Gall

The general formulation of our temporal network allows to rely on any multi person pose estimation approach as spatial network.

Multi-Person Pose Estimation Pose Tracking

PoseTrack: A Benchmark for Human Pose Estimation and Tracking

2 code implementations CVPR 2018 Mykhaylo Andriluka, Umar Iqbal, Eldar Insafutdinov, Leonid Pishchulin, Anton Milan, Juergen Gall, Bernt Schiele

In this work, we aim to further advance the state of the art by establishing "PoseTrack", a new large-scale benchmark for video-based human pose estimation and articulated tracking, and bringing together the community of researchers working on visual human analysis.

Activity Recognition Multi-Person Pose Estimation +1

A Dual-Source Approach for 3D Human Pose Estimation from a Single Image

no code implementations8 May 2017 Umar Iqbal, Andreas Doering, Hashim Yasin, Björn Krüger, Andreas Weber, Juergen Gall

To this end, we first convert the motion capture data into a normalized 2D pose space, and separately learn a 2D pose estimation model from the image data.

Monocular 3D Human Pose Estimation Pose Retrieval

PoseTrack: Joint Multi-Person Pose Estimation and Tracking

1 code implementation CVPR 2017 Umar Iqbal, Anton Milan, Juergen Gall

In this work, we introduce the challenging problem of joint multi-person pose estimation and tracking of an unknown number of persons in unconstrained videos.

Multi-Person Pose Estimation Multi-Person Pose Estimation and Tracking +1

Pose for Action - Action for Pose

no code implementations13 Mar 2016 Umar Iqbal, Martin Garbade, Juergen Gall

In this work we propose to utilize information about human actions to improve pose estimation in monocular videos.

Action Recognition Pose Estimation +1

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