no code implementations • 27 Dec 2023 • Enes Duran, Muhammed Kocabas, Vasileios Choutas, Zicong Fan, Michael J. Black
Therefore, we develop a generative motion prior specific for hands, trained on the AMASS dataset which features diverse and high-quality hand motions.
Ranked #4 on
3D Hand Pose Estimation
on HO-3D v3
1 code implementation • CVPR 2024 • Zicong Fan, Maria Parelli, Maria Eleni Kadoglou, Muhammed Kocabas, Xu Chen, Michael J. Black, Otmar Hilliges
Since humans interact with diverse objects every day, the holistic 3D capture of these interactions is important to understand and model human behaviour.
1 code implementation • CVPR 2024 • Muhammed Kocabas, Jen-Hao Rick Chang, James Gabriel, Oncel Tuzel, Anurag Ranjan
We achieve state-of-the-art rendering quality with a rendering speed of 60 FPS while being ~100x faster to train over previous work.
no code implementations • 20 Oct 2023 • Muhammed Kocabas, Ye Yuan, Pavlo Molchanov, Yunrong Guo, Michael J. Black, Otmar Hilliges, Jan Kautz, Umar Iqbal
This design combines the strengths of SLAM and motion priors, which leads to significant improvements in human and camera motion estimation.
no code implementations • 14 Sep 2023 • Jona Braun, Sammy Christen, Muhammed Kocabas, Emre Aksan, Otmar Hilliges
Through a hierarchical framework, we first learn skill priors for both body and hand movements in a decoupled setting.
no code implementations • 6 Sep 2022 • Xi Wang, Gen Li, Yen-Ling Kuo, Muhammed Kocabas, Emre Aksan, Otmar Hilliges
We further qualitatively evaluate the effectiveness of our method on real images and demonstrate its generalizability towards interaction types and object categories.
1 code implementation • 1 Sep 2022 • Andrea Ziani, Zicong Fan, Muhammed Kocabas, Sammy Christen, Otmar Hilliges
We introduce TempCLR, a new time-coherent contrastive learning approach for the structured regression task of 3D hand reconstruction.
1 code implementation • CVPR 2023 • Zicong Fan, Omid Taheri, Dimitrios Tzionas, Muhammed Kocabas, Manuel Kaufmann, Michael J. Black, Otmar Hilliges
In part this is because there exist no datasets with ground-truth 3D annotations for the study of physically consistent and synchronised motion of hands and articulated objects.
1 code implementation • CVPR 2022 • Hongwei Yi, Chun-Hao P. Huang, Dimitrios Tzionas, Muhammed Kocabas, Mohamed Hassan, Siyu Tang, Justus Thies, Michael J. Black
In fact, we demonstrate that these human-scene interactions (HSIs) can be leveraged to improve the 3D reconstruction of a scene from a monocular RGB video.
1 code implementation • CVPR 2022 • Sammy Christen, Muhammed Kocabas, Emre Aksan, Jemin Hwangbo, Jie Song, Otmar Hilliges
We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose.
1 code implementation • ICCV 2021 • Sai Kumar Dwivedi, Nikos Athanasiou, Muhammed Kocabas, Michael J. Black
For Minimally-Clothed regions, we define the DSR-MC loss, which encourages a tight match between a rendered SMPL body and the minimally-clothed regions of the image.
Ranked #61 on
3D Human Pose Estimation
on 3DPW
(using extra training data)
1 code implementation • ICCV 2021 • Muhammed Kocabas, Chun-Hao P. Huang, Joachim Tesch, Lea Müller, Otmar Hilliges, Michael J. Black
We then train a novel network that concatenates the camera calibration to the image features and uses these together to regress 3D body shape and pose.
Ranked #1 on
3D Multi-Person Pose Estimation
on AGORA
1 code implementation • 1 Jul 2021 • Zicong Fan, Adrian Spurr, Muhammed Kocabas, Siyu Tang, Michael J. Black, Otmar Hilliges
In natural conversation and interaction, our hands often overlap or are in contact with each other.
Ranked #7 on
3D Interacting Hand Pose Estimation
on InterHand2.6M
1 code implementation • ICCV 2021 • Muhammed Kocabas, Chun-Hao P. Huang, Otmar Hilliges, Michael J. Black
Despite significant progress, we show that state of the art 3D human pose and shape estimation methods remain sensitive to partial occlusion and can produce dramatically wrong predictions although much of the body is observable.
Ranked #2 on
3D Multi-Person Pose Estimation
on AGORA
3D human pose and shape estimation
3D Multi-Person Pose Estimation
no code implementations • ICLR 2020 • Modar Alfadly, Adel Bibi, Muhammed Kocabas, Bernard Ghanem
In this work, we propose a new training regularizer that aims to minimize the probabilistic expected training loss of a DNN subject to a generic Gaussian input.
5 code implementations • CVPR 2020 • Muhammed Kocabas, Nikos Athanasiou, Michael J. Black
Human motion is fundamental to understanding behavior.
Ranked #34 on
Monocular 3D Human Pose Estimation
on Human3.6M
1 code implementation • CVPR 2019 • Muhammed Kocabas, Salih Karagoz, Emre Akbas
Training accurate 3D human pose estimators requires large amount of 3D ground-truth data which is costly to collect.
Ranked #1 on
Weakly-supervised 3D Human Pose Estimation
on Human3.6M
(Number of Frames Per View metric)
Self-Supervised Learning
Weakly-supervised 3D Human Pose Estimation
4 code implementations • ECCV 2018 • Muhammed Kocabas, Salih Karagoz, Emre Akbas
In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose estimation architecture that combines a multi-task model with a novel assignment method.
Ranked #8 on
Multi-Person Pose Estimation
on MS COCO