Search Results for author: Siyu Tang

Found 35 papers, 16 papers with code

A Skeleton-Driven Neural Occupancy Representation for Articulated Hands

no code implementations23 Sep 2021 Korrawe Karunratanakul, Adrian Spurr, Zicong Fan, Otmar Hilliges, Siyu Tang

We present Hand ArticuLated Occupancy (HALO), a novel representation of articulated hands that bridges the advantages of 3D keypoints and neural implicit surfaces and can be used in end-to-end trainable architectures.

The Power of Points for Modeling Humans in Clothing

no code implementations ICCV 2021 Qianli Ma, Jinlong Yang, Siyu Tang, Michael J. Black

The geometry feature can be optimized to fit a previously unseen scan of a person in clothing, enabling the scan to be reposed realistically.

Learning Motion Priors for 4D Human Body Capture in 3D Scenes

no code implementations ICCV 2021 Siwei Zhang, Yan Zhang, Federica Bogo, Marc Pollefeys, Siyu Tang

To prove the effectiveness of the proposed motion priors, we combine them into a novel pipeline for 4D human body capture in 3D scenes.

Motion Capture

MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images

no code implementations NeurIPS 2021 Shaofei Wang, Marko Mihajlovic, Qianli Ma, Andreas Geiger, Siyu Tang

In contrast, we propose an approach that can quickly generate realistic clothed human avatars, represented as controllable neural SDFs, given only monocular depth images.


Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration

no code implementations CVPR 2021 Shaofei Wang, Andreas Geiger, Siyu Tang

We combine PTF with multi-class occupancy networks, obtaining a novel learning-based framework that learns to simultaneously predict shape and per-point correspondences between the posed space and the canonical space for clothed human.

Surface Reconstruction Translation

LEAP: Learning Articulated Occupancy of People

no code implementations CVPR 2021 Marko Mihajlovic, Yan Zhang, Michael J. Black, Siyu Tang

Substantial progress has been made on modeling rigid 3D objects using deep implicit representations.

On Self-Contact and Human Pose

no code implementations CVPR 2021 Lea Müller, Ahmed A. A. Osman, Siyu Tang, Chun-Hao P. Huang, Michael J. Black

Third, we develop a novel HPS optimization method, SMPLify-XMC, that includes contact constraints and uses the known 3DCP body pose during fitting to create near ground-truth poses for MTP images.

Pose Estimation

We are More than Our Joints: Predicting how 3D Bodies Move

no code implementations CVPR 2021 Yan Zhang, Michael J. Black, Siyu Tang

We note that motion prediction methods accumulate errors over time, resulting in joints or markers that diverge from true human bodies.

Human motion prediction Motion Capture +2

MATE: Plugging in Model Awareness to Task Embedding for Meta Learning

1 code implementation NeurIPS 2020 Xiaohan Chen, Zhangyang Wang, Siyu Tang, Krikamol Muandet

Meta-learning improves generalization of machine learning models when faced with previously unseen tasks by leveraging experiences from different, yet related prior tasks.

Feature Selection Few-Shot Learning

4D Human Body Capture from Egocentric Video via 3D Scene Grounding

no code implementations26 Nov 2020 Miao Liu, Dexin Yang, Yan Zhang, Zhaopeng Cui, James M. Rehg, Siyu Tang

We introduce a novel task of reconstructing a time series of second-person 3D human body meshes from monocular egocentric videos.

Motion Capture Time Series

PLACE: Proximity Learning of Articulation and Contact in 3D Environments

1 code implementation12 Aug 2020 Siwei Zhang, Yan Zhang, Qianli Ma, Michael J. Black, Siyu Tang

To synthesize realistic human-scene interactions, it is essential to effectively represent the physical contact and proximity between the body and the world.

Grasping Field: Learning Implicit Representations for Human Grasps

2 code implementations10 Aug 2020 Korrawe Karunratanakul, Jinlong Yang, Yan Zhang, Michael Black, Krikamol Muandet, Siyu Tang

Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud.

3D Object Reconstruction Grasp Generation +1

Perpetual Motion: Generating Unbounded Human Motion

no code implementations27 Jul 2020 Yan Zhang, Michael J. Black, Siyu Tang

To address this problem, we propose a model to generate non-deterministic, \textit{ever-changing}, perpetual human motion, in which the global trajectory and the body pose are cross-conditioned.

Motion Estimation Time Series

Generating 3D People in Scenes without People

3 code implementations CVPR 2020 Yan Zhang, Mohamed Hassan, Heiko Neumann, Michael J. Black, Siyu Tang

However, this is a challenging task for a computer as solving it requires that (1) the generated human bodies to be semantically plausible within the 3D environment (e. g. people sitting on the sofa or cooking near the stove), and (2) the generated human-scene interaction to be physically feasible such that the human body and scene do not interpenetrate while, at the same time, body-scene contact supports physical interactions.

Pose Estimation

Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video

1 code implementation ECCV 2020 Miao Liu, Siyu Tang, Yin Li, James Rehg

Motivated by this, we adopt intentional hand movement as a future representation and propose a novel deep network that jointly models and predicts the egocentric hand motion, interaction hotspots and future action.

Action Anticipation Human-Object Interaction Detection

Learning Multi-Human Optical Flow

2 code implementations24 Oct 2019 Anurag Ranjan, David T. Hoffmann, Dimitrios Tzionas, Siyu Tang, Javier Romero, Michael J. Black

Therefore, we develop a dataset of multi-human optical flow and train optical flow networks on this dataset.

Motion Capture Optical Flow Estimation

Learning to Train with Synthetic Humans

2 code implementations2 Aug 2019 David T. Hoffmann, Dimitrios Tzionas, Micheal J. Black, Siyu Tang

Here we explore two variations of synthetic data for this challenging problem; a dataset with purely synthetic humans and a real dataset augmented with synthetic humans.

Pose Estimation

Frontal Low-rank Random Tensors for Fine-grained Action Segmentation

1 code implementation3 Jun 2019 Yan Zhang, Krikamol Muandet, Qianli Ma, Heiko Neumann, Siyu Tang

In this paper, we propose an approach to representing high-order information for temporal action segmentation via a simple yet effective bilinear form.

Action Parsing Action Segmentation

End-to-end Learning for Graph Decomposition

no code implementations ICCV 2019 Jie Song, Bjoern Andres, Michael Black, Otmar Hilliges, Siyu Tang

The new optimization problem can be viewed as a Conditional Random Field (CRF) in which the random variables are associated with the binary edge labels of the initial graph and the hard constraints are introduced in the CRF as high-order potentials.

Multi-Person Pose Estimation

Local Temporal Bilinear Pooling for Fine-grained Action Parsing

1 code implementation CVPR 2019 Yan Zhang, Siyu Tang, Krikamol Muandet, Christian Jarvers, Heiko Neumann

Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period.

Action Parsing

Temporal Human Action Segmentation via Dynamic Clustering

1 code implementation15 Mar 2018 Yan Zhang, He Sun, Siyu Tang, Heiko Neumann

We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and patient monitoring.

Action Segmentation

Multiple People Tracking by Lifted Multicut and Person Re-Identification

no code implementations CVPR 2017 Siyu Tang, Mykhaylo Andriluka, Bjoern Andres, Bernt Schiele

This allows us to reward tracks that assign detections of similar appearance to the same person in a way that does not introduce implausible solutions.

Multiple People Tracking Person Re-Identification +1

Generating Descriptions with Grounded and Co-Referenced People

no code implementations CVPR 2017 Anna Rohrbach, Marcus Rohrbach, Siyu Tang, Seong Joon Oh, Bernt Schiele

At training time, we first learn how to localize characters by relating their visual appearance to mentions in the descriptions via a semi-supervised approach.

Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications

1 code implementation14 Nov 2016 Evgeny Levinkov, Jonas Uhrig, Siyu Tang, Mohamed Omran, Eldar Insafutdinov, Alexander Kirillov, Carsten Rother, Thomas Brox, Bernt Schiele, Bjoern Andres

In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time.

Combinatorial Optimization Multiple Object Tracking +2

Multi-Person Tracking by Multicut and Deep Matching

no code implementations17 Aug 2016 Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Bernt Schiele

In [1], we proposed a graph-based formulation that links and clusters person hypotheses over time by solving a minimum cost subgraph multicut problem.

A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects

no code implementations21 Jul 2016 Margret Keuper, Siyu Tang, Yu Zhongjie, Bjoern Andres, Thomas Brox, Bernt Schiele

Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios.

Motion Segmentation Object Detection

Subgraph Decomposition for Multi-Target Tracking

no code implementations CVPR 2015 Siyu Tang, Bjoern Andres, Miykhaylo Andriluka, Bernt Schiele

Tracking multiple targets in a video, based on a finite set of detection hypotheses, is a persistent problem in computer vision.

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