no code implementations • 28 Feb 2024 • Santosh Thoduka, Nico Hochgeschwender, Juergen Gall, Paul G. Plöger
To address this deficit, we present the multimodal Handover Failure Detection dataset, which consists of failures induced by the human participant, such as ignoring the robot or not releasing the object.
no code implementations • 23 Dec 2023 • Lokesh Veeramacheneni, Moritz Wolter, Juergen Gall
In response, we propose a new frequency band-based quality metric, which opens a door into the frequency domain yet, at the same time, preserves spatial aspects of the data.
no code implementations • 14 Dec 2023 • Shijie Li, Farhad G. Zanjani, Haitam Ben Yahia, Yuki M. Asano, Juergen Gall, Amirhossein Habibian
This is because the source-view images and corresponding poses are processed separately and injected into the model at different stages.
no code implementations • 27 Nov 2023 • Zeyun Zhong, Chengzhi Wu, Manuel Martin, Michael Voit, Juergen Gall, Jürgen Beyerer
However, the majority of existing action anticipation models adhere to a deterministic approach, neglecting to account for future uncertainties.
no code implementations • 29 Sep 2023 • Zeyun Zhong, Manuel Martin, Michael Voit, Juergen Gall, Jürgen Beyerer
The ability to anticipate possible future human actions is essential for a wide range of applications, including autonomous driving and human-robot interaction.
no code implementations • 14 Sep 2023 • Rong Li, Shijie Li, Xieyuanli Chen, Teli Ma, Juergen Gall, Junwei Liang
In this paper, we present TFNet, a range-image-based LiDAR semantic segmentation method that utilizes temporal information to address this issue.
Ranked #1 on Semantic Segmentation on SemanticPOSS
no code implementations • 22 Aug 2023 • Shijie Li, Rong Li, Juergen Gall
In this paper, we therefore propose a generator for multi-modal data that separates modal-independent information of the semantic layout from the modal-dependent information that is needed to generate an RGB and a depth image, respectively.
1 code implementation • ICCV 2023 • Emad Bahrami, Gianpiero Francesca, Juergen Gall
In this work, we try to answer how much long-term temporal context is required for temporal action segmentation by introducing a transformer-based model that leverages sparse attention to capture the full context of a video.
Ranked #1 on Action Segmentation on Assembly101
no code implementations • ICCV 2023 • Vadim Sushko, Ruyu Wang, Juergen Gall
The task of few-shot GAN adaptation aims to adapt a pre-trained GAN model to a small dataset with very few training images.
1 code implementation • ICCV 2023 • Shuxiao Ding, Eike Rehder, Lukas Schneider, Marius Cordts, Juergen Gall
Tracking 3D objects accurately and consistently is crucial for autonomous vehicles, enabling more reliable downstream tasks such as trajectory prediction and motion planning.
1 code implementation • 26 Jun 2023 • Olga Zatsarynna, Juergen Gall
In this paper, we address the problem of short-term action anticipation, i. e., we want to predict an upcoming action one second before it happens.
Ranked #1 on Action Anticipation on Assembly101
1 code implementation • 19 Jun 2023 • Peizheng Li, Shuxiao Ding, Xieyuanli Chen, Niklas Hanselmann, Marius Cordts, Juergen Gall
Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic.
no code implementations • 9 Jun 2023 • Andreas Doering, Juergen Gall
Multi-person pose tracking is an important element for many applications and requires to estimate the human poses of all persons in a video and to track them over time.
1 code implementation • ICCV 2023 • Julian Tanke, Linguang Zhang, Amy Zhao, Chengcheng Tang, Yujun Cai, Lezi Wang, Po-Chen Wu, Juergen Gall, Cem Keskin
We propose Social Diffusion, a novel method for short-term and long-term forecasting of the motion of multiple persons as well as their social interactions.
1 code implementation • 16 Dec 2022 • Mohamad Hakam Shams Eddin, Ribana Roscher, Juergen Gall
Climate change is expected to intensify and increase extreme events in the weather cycle.
no code implementations • 12 Oct 2022 • Yaser Souri, Yazan Abu Farha, Emad Bahrami, Gianpiero Francesca, Juergen Gall
As obtaining annotations to train an approach for action segmentation in a fully supervised way is expensive, various approaches have been proposed to train action segmentation models using different forms of weak supervision, e. g., action transcripts, action sets, or more recently timestamps.
1 code implementation • 8 Oct 2022 • Shijie Li, Ming-Ming Cheng, Juergen Gall
The goal of semantic image synthesis is to generate photo-realistic images from semantic label maps.
no code implementations • 24 Sep 2022 • Olga Zatsarynna, Yazan Abu Farha, Juergen Gall
Distinguishing if an action is performed as intended or if an intended action fails is an important skill that not only humans have, but that is also important for intelligent systems that operate in human environments.
1 code implementation • 15 Sep 2022 • Vadim Sushko, Dan Zhang, Juergen Gall, Anna Khoreva
To this end, inspired by the recent architectural developments of single-image GANs, we introduce our OSMIS model which enables the synthesis of segmentation masks that are precisely aligned to the generated images in the one-shot regime.
2 code implementations • 1 Sep 2022 • Nadine Behrmann, S. Alireza Golestaneh, Zico Kolter, Juergen Gall, Mehdi Noroozi
This paper introduces a unified framework for video action segmentation via sequence to sequence (seq2seq) translation in a fully and timestamp supervised setup.
Ranked #4 on Action Segmentation on Assembly101
no code implementations • 14 Jun 2022 • Rania Briq, Chuhang Zou, Leonid Pishchulin, Chris Broaddus, Juergen Gall
We consider the problem of synthesizing multi-action human motion sequences of arbitrary lengths.
1 code implementation • 27 Jan 2022 • David T. Hoffmann, Nadine Behrmann, Juergen Gall, Thomas Brox, Mehdi Noroozi
This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a new member in the family of InfoNCE losses that preserves a ranked ordering of positive samples.
1 code implementation • 3DV 2021 • Julian Tanke, Chintan Zaveri, Juergen Gall
Recently, a few works have been proposed to model the uncertainty of the future human motion.
Ranked #10 on Human Pose Forecasting on Human3.6M
1 code implementation • 30 Nov 2021 • Mohsen Fayyaz, Soroush Abbasi Koohpayegani, Farnoush Rezaei Jafari, Sunando Sengupta, Hamid Reza Vaezi Joze, Eric Sommerlade, Hamed Pirsiavash, Juergen Gall
Since ATS is a parameter-free module, it can be added to the off-the-shelf pre-trained vision transformers as a plug and play module, thus reducing their GFLOPs without any additional training.
Ranked #13 on Efficient ViTs on ImageNet-1K (with DeiT-S)
1 code implementation • 16 Nov 2021 • Di Chen, Andreas Doering, Shanshan Zhang, Jian Yang, Juergen Gall, Bernt Schiele
Video-based person re-identification (re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras.
Representation Learning Video-Based Person Re-Identification
no code implementations • 27 Oct 2021 • Saber Pourheydari, Emad Bahrami, Mohsen Fayyaz, Gianpiero Francesca, Mehdi Noroozi, Juergen Gall
While recurrent neural networks (RNNs) demonstrate outstanding capabilities for future video frame prediction, they model dynamics in a discrete time space, i. e., they predict the frames sequentially with a fixed temporal step.
no code implementations • ICCV 2021 • Nadine Behrmann, Mohsen Fayyaz, Juergen Gall, Mehdi Noroozi
We argue that a single representation to capture both types of features is sub-optimal, and propose to decompose the representation space into stationary and non-stationary features via contrastive learning from long and short views, i. e. long video sequences and their shorter sub-sequences.
no code implementations • 9 Aug 2021 • Yaser Souri, Yazan Abu Farha, Fabien Despinoy, Gianpiero Francesca, Juergen Gall
We apply FIFA on top of state-of-the-art approaches for weakly supervised action segmentation and alignment as well as fully supervised action segmentation.
Segmentation Weakly Supervised Action Segmentation (Transcript)
1 code implementation • 29 Jul 2021 • Santosh Thoduka, Juergen Gall, Paul G. Plöger
Our method learns to predict the motions that occur during the nominal execution of a task, including camera and robot body motion.
no code implementations • 18 Jul 2021 • Olga Zatsarynna, Yazan Abu Farha, Juergen Gall
This poses a problem for domains such as autonomous driving, where the reaction time is crucial.
Ranked #8 on Action Anticipation on EPIC-KITCHENS-100 (test)
no code implementations • 5 Jul 2021 • Rania Briq, Pratika Kochar, Juergen Gall
This paper proposes an approach that generates multiple 3D human meshes from text.
1 code implementation • 12 May 2021 • Vadim Sushko, Juergen Gall, Anna Khoreva
Training GANs in low-data regimes remains a challenge, as overfitting often leads to memorization or training divergence.
1 code implementation • 24 Mar 2021 • Vadim Sushko, Dan Zhang, Juergen Gall, Anna Khoreva
In this work, we introduce SIV-GAN, an unconditional generative model that can generate new scene compositions from a single training image or a single video clip.
1 code implementation • CVPR 2021 • Zhe Li, Yazan Abu Farha, Juergen Gall
To demonstrate the effectiveness of timestamp supervision, we propose an approach to train a segmentation model using only timestamps annotations.
Ranked #4 on Weakly Supervised Action Localization on GTEA
1 code implementation • 24 Jan 2021 • Julian Tanke, Juergen Gall
In this work we propose an approach for estimating 3D human poses of multiple people from a set of calibrated cameras.
1 code implementation • 21 Jan 2021 • Sovan Biswas, Juergen Gall
Since computing, the probabilities for the full power set becomes intractable as the number of action classes increases, we assign an action set to each detected person under the constraint that the assignment is consistent with the annotation of the video clip.
no code implementations • 21 Jan 2021 • Sovan Biswas, Yaser Souri, Juergen Gall
In this paper, we propose an approach that spatially localizes the activities in a video frame where each person can perform multiple activities at the same time.
no code implementations • ICCV 2021 • Shijie Li, Yanying Zhou, Jinhui Yi, Juergen Gall
Trajectory forecasting is a crucial step for autonomous vehicles and mobile robots in order to navigate and interact safely.
1 code implementation • ICLR 2021 • Vadim Sushko, Edgar Schönfeld, Dan Zhang, Juergen Gall, Bernt Schiele, Anna Khoreva
By providing stronger supervision to the discriminator as well as to the generator through spatially- and semantically-aware discriminator feedback, we are able to synthesize images of higher fidelity with better alignment to their input label maps, making the use of the perceptual loss superfluous.
1 code implementation • CVPR 2021 • Mohsen Fayyaz, Emad Bahrami, Ali Diba, Mehdi Noroozi, Ehsan Adeli, Luc van Gool, Juergen Gall
While the GFLOPs of a 3D CNN can be decreased by reducing the temporal feature resolution within the network, there is no setting that is optimal for all input clips.
no code implementations • 12 Nov 2020 • Andreas Doering, Di Chen, Shanshan Zhang, Bernt Schiele, Juergen Gall
For that reason, we present PoseTrackReID, a large-scale dataset for multi-person pose tracking and video-based person re-ID.
no code implementations • 11 Nov 2020 • Nadine Behrmann, Juergen Gall, Mehdi Noroozi
This paper introduces a novel method for self-supervised video representation learning via feature prediction.
no code implementations • 14 Oct 2020 • Shijie Li, Jinhui Yi, Yazan Abu Farha, Juergen Gall
To this end, the network first refines the poses before they are further processed to recognize the action.
no code implementations • 2 Sep 2020 • Yazan Abu Farha, Qiuhong Ke, Bernt Schiele, Juergen Gall
With the success of deep learning methods in analyzing activities in videos, more attention has recently been focused towards anticipating future activities.
2 code implementations • 20 Aug 2020 • Shijie Li, Xieyuanli Chen, Yun Liu, Dengxin Dai, Cyrill Stachniss, Juergen Gall
Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources.
Ranked #2 on Real-Time 3D Semantic Segmentation on SemanticKITTI
no code implementations • 11 Aug 2020 • Alexander Richard, Colin Lea, Shugao Ma, Juergen Gall, Fernando de la Torre, Yaser Sheikh
Codec Avatars are a recent class of learned, photorealistic face models that accurately represent the geometry and texture of a person in 3D (i. e., for virtual reality), and are almost indistinguishable from video.
1 code implementation • 10 Aug 2020 • Shijie Li, Yun Liu, Juergen Gall
Many point-based semantic segmentation methods have been designed for indoor scenarios, but they struggle if they are applied to point clouds that are captured by a LiDAR sensor in an outdoor environment.
1 code implementation • 16 Jun 2020 • Shijie Li, Yazan Abu Farha, Yun Liu, Ming-Ming Cheng, Juergen Gall
Despite the capabilities of these approaches in capturing temporal dependencies, their predictions suffer from over-segmentation errors.
Ranked #5 on Action Segmentation on Assembly101
no code implementations • 19 May 2020 • Yaser Souri, Alexander Richard, Luca Minciullo, Juergen Gall
Action segmentation is the task of temporally segmenting every frame of an untrimmed video.
no code implementations • 1 May 2020 • Yifei Zhang, Rania Briq, Julian Tanke, Juergen Gall
This work focuses on synthesizing human poses from human-level text descriptions.
no code implementations • ECCV 2020 • Umer Rafi, Andreas Doering, Bastian Leibe, Juergen Gall
Instead of training the network for estimating keypoint correspondences on video data, it is trained on a large scale image datasets for human pose estimation using self-supervision.
Multi-Person Pose Estimation Multi-Person Pose Estimation and Tracking +1
1 code implementation • CVPR 2020 • Mohsen Fayyaz, Juergen Gall
In addition, the network estimates the action labels for each frame.
no code implementations • 30 Dec 2019 • Olga Zatsarynna, Johann Sawatzky, Juergen Gall
On unlabeled images, we predict a probability map for latent classes and use it as a supervision signal to learn semantic segmentation.
no code implementations • 13 Dec 2019 • Julian Tanke, Oh-Hun Kwon, Patrick Stotko, Radu Alexandru Rosu, Michael Weinmann, Hassan Errami, Sven Behnke, Maren Bennewitz, Reinhard Klein, Andreas Weber, Angela Yao, Juergen Gall
The key prerequisite for accessing the huge potential of current machine learning techniques is the availability of large databases that capture the complex relations of interest.
1 code implementation • 13 Dec 2019 • Pau Panareda Busto, Juergen Gall
The estimation of viewpoints and keypoints effectively enhance object detection methods by extracting valuable traits of the object instances.
no code implementations • 12 Dec 2019 • Julian Tanke, Andreas Weber, Juergen Gall
We exploit this connection by first anticipating symbolic labels and then generate human motion, conditioned on the human motion input sequence as well as on the forecast labels.
no code implementations • 10 Oct 2019 • Fida Mohammad Thoker, Juergen Gall
To this end, we extract the knowledge of the trained teacher network for the source modality and transfer it to a small ensemble of student networks for the target modality.
no code implementations • 26 Aug 2019 • Yazan Abu Farha, Juergen Gall
Anticipating future activities in video is a task with many practical applications.
1 code implementation • 30 Jul 2019 • Pau Panareda Busto, Ahsan Iqbal, Juergen Gall
Since this assumption is violated under real-world conditions, we propose an approach for open set domain adaptation where the target domain contains instances of categories that are not present in the source domain.
no code implementations • 3 Jun 2019 • Hilde Kuehne, Alexander Richard, Juergen Gall
Action recognition has become a rapidly developing research field within the last decade.
1 code implementation • 3 Jun 2019 • Hilde Kuehne, Ahsan Iqbal, Alexander Richard, Juergen Gall
Action recognition is so far mainly focusing on the problem of classification of hand selected preclipped actions and reaching impressive results in this field.
no code implementations • 16 May 2019 • Johann Sawatzky, Debayan Banerjee, Juergen Gall
They do not require additional curation as it is the case for the clean class tags used by current weakly supervised approaches and they provide textual context for the classes present in an image.
no code implementations • 15 May 2019 • Yueh-Tung Chen, Martin Garbade, Juergen Gall
We address the task of 3D semantic scene completion, i. e. , given a single depth image, we predict the semantic labels and occupancy of voxels in a 3D grid representing the scene.
no code implementations • 26 Apr 2019 • Rania Briq, Andreas Doering, Juergen Gall
We propose a joint model of human joint detection and association for 2D multi-person pose estimation (MPPE).
2 code implementations • CVPR 2019 • Anna Kukleva, Hilde Kuehne, Fadime Sener, Juergen Gall
The task of temporally detecting and segmenting actions in untrimmed videos has seen an increased attention recently.
1 code implementation • CVPR 2019 • Johann Sawatzky, Yaser Souri, Christian Grund, Juergen Gall
When humans have to solve everyday tasks, they simply pick the objects that are most suitable.
1 code implementation • 5 Apr 2019 • Yaser Souri, Mohsen Fayyaz, Luca Minciullo, Gianpiero Francesca, Juergen Gall
Action segmentation is the task of predicting the actions for each frame of a video.
Segmentation Weakly Supervised Action Segmentation (Transcript)
5 code implementations • ICCV 2019 • Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, Juergen Gall
Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR.
Ranked #32 on 3D Semantic Segmentation on SemanticKITTI
2 code implementations • CVPR 2019 • Yazan Abu Farha, Juergen Gall
Temporally locating and classifying action segments in long untrimmed videos is of particular interest to many applications like surveillance and robotics.
Ranked #20 on Action Segmentation on GTEA
2 code implementations • 13 Dec 2018 • Moritz Wolter, Juergen Gall, Angela Yao
Fourier methods have a long and proven track record as an excellent tool in data processing.
1 code implementation • 13 Dec 2018 • Alejandro Hernandez Ruiz, Juergen Gall, Francesc Moreno-Noguer
First, we represent the data using a spatio-temporal tensor of 3D skeleton coordinates which allows formulating the prediction problem as an inpainting one, for which GANs work particularly well.
1 code implementation • 24 Jul 2018 • Rania Briq, Michael Moeller, Juergen Gall
Weakly supervised semantic segmentation has been a subject of increased interest due to the scarcity of fully annotated images.
no code implementations • 21 Jun 2018 • Michael Moeller, Otmar Loffeld, Juergen Gall, Felix Krahmer
The idea of compressed sensing is to exploit representations in suitable (overcomplete) dictionaries that allow to recover signals far beyond the Nyquist rate provided that they admit a sparse representation in the respective dictionary.
no code implementations • ECCV 2018 • Ali Diba, Mohsen Fayyaz, Vivek Sharma, M. Mahdi Arzani, Rahman Yousefzadeh, Juergen Gall, Luc van Gool
Our experiments show that adding STC blocks to current state-of-the-art architectures outperforms the state-of-the-art methods on the HMDB51, UCF101 and Kinetics datasets.
no code implementations • CVPR 2018 • Alexander Richard, Hilde Kuehne, Ahsan Iqbal, Juergen Gall
Video learning is an important task in computer vision and has experienced increasing interest over the recent years.
no code implementations • 11 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.
no code implementations • ECCV 2018 • Umar Iqbal, Pavlo Molchanov, Thomas Breuel, Juergen Gall, Jan Kautz
Estimating the 3D pose of a hand is an essential part of human-computer interaction.
no code implementations • 10 Apr 2018 • Martin Garbade, Yueh-Tung Chen, Johann Sawatzky, Juergen Gall
In this work, we propose a two stream approach that leverages depth information and semantic information, which is inferred from the RGB image, for this task.
Ranked #7 on 3D Semantic Scene Completion on SemanticKITTI
3D Semantic Scene Completion Vocal Bursts Valence Prediction
1 code implementation • CVPR 2018 • Yazan Abu Farha, Alexander Richard, Juergen Gall
Analyzing human actions in videos has gained increased attention recently.
no code implementations • 6 Feb 2018 • Sovan Biswas, Juergen Gall
In this paper, we propose a structural recurrent neural network (SRNN) that uses a series of interconnected RNNs to jointly capture the actions of individuals, their interactions, as well as the group activity.
no code implementations • 9 Nov 2017 • Grigorios Kalliatakis, Anca Sticlaru, George Stamatiadis, Shoaib Ehsan, Ales Leonardis, Juergen Gall, Klaus D. McDonald-Maier
We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than real-world data.
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.
Ranked #3 on Multi-Person Pose Estimation on PoseTrack2017
no code implementations • ICCV 2017 • Pau Panareda Busto, Juergen Gall
The approach learns a mapping from the source to the target domain by jointly solving an assignment problem that labels those target instances that potentially belong to the categories of interest present in the source dataset.
3 code implementations • ICCV 2017 • Mengqi Ji, Juergen Gall, Haitian Zheng, Yebin Liu, Lu Fang
It takes a set of images and their corresponding camera parameters as input and directly infers the 3D model.
no code implementations • 10 Jul 2017 • Johann Sawatzky, Juergen Gall
The concept of affordance is important to understand the relevance of object parts for a certain functional interaction.
1 code implementation • CVPR 2017 • Johann Sawatzky, Abhilash Srikantha, Juergen Gall
Localizing functional regions of objects or affordances is an important aspect of scene understanding and relevant for many robotics applications.
no code implementations • 27 Jun 2017 • Ahsan Iqbal, Alexander Richard, Hilde Kuehne, Juergen Gall
In this work, we propose a novel recurrent ConvNet architecture called recurrent residual networks to address the task of action recognition.
1 code implementation • CVPR 2018 • Alexander Richard, Hilde Kuehne, Juergen Gall
Action detection and temporal segmentation of actions in videos are topics of increasing interest.
no code implementations • 8 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.
Ranked #37 on Monocular 3D Human Pose Estimation on Human3.6M
no code implementations • 3 Apr 2017 • Dimitrios Tzionas, Juergen Gall
Benchmarking methods for 3d hand tracking is still an open problem due to the difficulty of acquiring ground truth data.
3 code implementations • ICCV 2015 • Dimitrios Tzionas, Juergen Gall
Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera.
2 code implementations • 3 Apr 2017 • Dimitrios Tzionas, Abhilash Srikantha, Pablo Aponte, Juergen Gall
In this work, we propose a framework for hand tracking that can capture the motion of two interacting hands using only a single, inexpensive RGB-D camera.
1 code implementation • CVPR 2017 • Alexander Richard, Hilde Kuehne, Juergen Gall
We present an approach for weakly supervised learning of human actions.
1 code implementation • 23 Mar 2017 • Alexander Richard, Juergen Gall
In this work, we propose a recurrent neural network that is equivalent to the traditional bag-of-words approach but enables for the application of discriminative training.
no code implementations • 12 Mar 2017 • Grigorios Kalliatakis, Georgios Stamatiadis, Shoaib Ehsan, Ales Leonardis, Juergen Gall, Anca Sticlaru, Klaus D. McDonald-Maier
Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention.
no code implementations • 12 Mar 2017 • Grigorios Kalliatakis, Shoaib Ehsan, Maria Fasli, Ales Leonardis, Juergen Gall, Klaus D. McDonald-Maier
We conduct a rigorous evaluation on a common ground by combining this dataset with different state-of-the-art deep convolutional architectures in order to achieve recognition of human rights violations.
2 code implementations • 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.
Ranked #1 on Pose Tracking on Multi-Person PoseTrack
Multi-Person Pose Estimation Multi-Person Pose Estimation and Tracking +1
no code implementations • 7 Oct 2016 • Hilde Kuehne, Alexander Richard, Juergen Gall
Our system is based on the idea that, given a sequence of input data and a transcript, i. e. a list of the order the actions occur in the video, it is possible to infer the actions within the video stream, and thus, learn the related action models without the need for any frame-based annotation.
no code implementations • 6 Sep 2016 • Dimitrios Tzionas, Juergen Gall
Although commercial and open-source software exist to reconstruct a static object from a sequence recorded with an RGB-D sensor, there is a lack of tools that build rigged models of articulated objects that deform realistically and can be used for tracking or animation.
1 code implementation • 30 Aug 2016 • Umar Iqbal, Juergen Gall
To this end, we consider multi-person pose estimation as a joint-to-person association problem.
Ranked #8 on Multi-Person Pose Estimation on MPII Multi-Person
1 code implementation • CVPR 2016 • Alexander Richard, Juergen Gall
While current approaches to action recognition on pre-segmented video clips already achieve high accuracies, temporal action detection is still far from comparably good results.
no code implementations • 10 May 2016 • Abhilash Srikantha, Juergen Gall
Localizing functional regions of objects or affordances is an important aspect of scene understanding.
no code implementations • 13 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.
Ranked #5 on Pose Estimation on UPenn Action
no code implementations • CVPR 2016 • Hashim Yasin, Umar Iqbal, Björn Krüger, Andreas Weber, Juergen Gall
To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient and robust 3D pose retrieval.
Ranked #22 on 3D Human Pose Estimation on HumanEva-I
no code implementations • 7 Sep 2015 • Hilde Kuehne, Juergen Gall, Thomas Serre
We describe an end-to-end generative approach for the segmentation and recognition of human activities.
no code implementations • 25 Aug 2015 • Hilde Kuehne, Juergen Gall, Thomas Serre
Through extensive system evaluations, we demonstrate that combining compact video representations based on Fisher Vectors with HMM-based modeling yields very significant gains in accuracy and when properly trained with sufficient training samples, structured temporal models outperform unstructured bag-of-word types of models by a large margin on the tested performance metric.
2 code implementations • 6 Jun 2015 • Dimitrios Tzionas, Luca Ballan, Abhilash Srikantha, Pablo Aponte, Marc Pollefeys, Juergen Gall
Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors.
no code implementations • CVPR 2015 • Marko Ristin, Juergen Gall, Matthieu Guillaumin, Luc van Gool
Compared to approaches that disregard the extra coarse labeled data, we achieve a relative improvement in subcategory classification accuracy of up to 22% in our large-scale image classification experiments.
no code implementations • In Proceedings British Machine Vision Conference 2014 (BMVC 2014) 2014 • Ilya Kostrikov, Juergen Gall
We address the problem of estimating the 3d pose from monocular images.
Ranked #24 on 3D Human Pose Estimation on HumanEva-I
no code implementations • CVPR 2014 • Marko Ristin, Matthieu Guillaumin, Juergen Gall, Luc van Gool
NCMFs not only outperform conventional random forests, but are also well suited for integrating new classes.
no code implementations • CVPR 2013 • Matthias Dantone, Juergen Gall, Christian Leistner, Luc van Gool
The second layer takes the estimated class distributions of the first one into account and is thereby able to predict joint locations by modeling the interdependence and co-occurrence of the parts.
no code implementations • NeurIPS 2011 • Angela Yao, Juergen Gall, Luc V. Gool, Raquel Urtasun
A common approach for handling the complexity and inherent ambiguities of 3D human pose estimation is to use pose priors learned from training data.