Search Results for author: Gerard Pons-Moll

Found 48 papers, 28 papers with code

NASA Neural Articulated Shape Approximation

no code implementations ECCV 2020 Boyang Deng, JP Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, Andrea Tagliasacchi

Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics.

Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing

1 code implementation ICCV 2021 Garvita Tiwari, Nikolaos Sarafianos, Tony Tung, Gerard Pons-Moll

Neural-GIF can be trained on raw 3D scans and reconstructs detailed complex surface geometry and deformations.

NRST: Non-rigid Surface Tracking from Monocular Video

no code implementations6 Jul 2021 Marc Habermann, Weipeng Xu, Helge Rhodin, Michael Zollhoefer, Gerard Pons-Moll, Christian Theobalt

Our texture term exploits the orientation information in the micro-structures of the objects, e. g., the yarn patterns of fabrics.

Real-time Deep Dynamic Characters

no code implementations4 May 2021 Marc Habermann, Lingjie Liu, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, Christian Theobalt

We propose a deep videorealistic 3D human character model displaying highly realistic shape, motion, and dynamic appearance learned in a new weakly supervised way from multi-view imagery.

Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes

1 code implementation CVPR 2021 Julian Chibane, Aayush Bansal, Verica Lazova, Gerard Pons-Moll

Recent neural view synthesis methods have achieved impressive quality and realism, surpassing classical pipelines which rely on multi-view reconstruction.

Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors

1 code implementation CVPR 2021 Vladimir Guzov, Aymen Mir, Torsten Sattler, Gerard Pons-Moll

We introduce (HPS) Human POSEitioning System, a method to recover the full 3D pose of a human registered with a 3D scan of the surrounding environment using wearable sensors.

3D Human Pose Estimation 3D Pose Estimation

SMPLicit: Topology-aware Generative Model for Clothed People

1 code implementation CVPR 2021 Enric Corona, Albert Pumarola, Guillem Alenyà, Gerard Pons-Moll, Francesc Moreno-Noguer

In this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry.

3D Reconstruction

Learning Speech-driven 3D Conversational Gestures from Video

no code implementations13 Feb 2021 Ikhsanul Habibie, Weipeng Xu, Dushyant Mehta, Lingjie Liu, Hans-Peter Seidel, Gerard Pons-Moll, Mohamed Elgharib, Christian Theobalt

We propose the first approach to automatically and jointly synthesize both the synchronous 3D conversational body and hand gestures, as well as 3D face and head animations, of a virtual character from speech input.

Hand Pose Estimation Motion Capture

Adjoint Rigid Transform Network: Task-conditioned Alignment of 3D Shapes

no code implementations1 Feb 2021 Keyang Zhou, Bharat Lal Bhatnagar, Bernt Schiele, Gerard Pons-Moll

The remarkable result is that with only self-supervision, ART facilitates learning a unique canonical orientation for both rigid and nonrigid shapes, which leads to a notable boost in performance of aforementioned tasks.

D-NeRF: Neural Radiance Fields for Dynamic Scenes

1 code implementation CVPR 2021 Albert Pumarola, Enric Corona, Gerard Pons-Moll, Francesc Moreno-Noguer

In this paper we introduce D-NeRF, a method that extends neural radiance fields to a dynamic domain, allowing to reconstruct and render novel images of objects under rigid and non-rigid motions from a \emph{single} camera moving around the scene.

Neural Rendering

SelfPose: 3D Egocentric Pose Estimation from a Headset Mounted Camera

1 code implementation2 Nov 2020 Denis Tome, Thiemo Alldieck, Patrick Peluse, Gerard Pons-Moll, Lourdes Agapito, Hernan Badino, Fernando de la Torre

The quantitative evaluation, on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric approaches.

Egocentric Pose Estimation Pose Estimation

Neural Unsigned Distance Fields for Implicit Function Learning

1 code implementation NeurIPS 2020 Julian Chibane, Aymen Mir, Gerard Pons-Moll

NDF represent surfaces at high resolutions as prior implicit models, but do not require closed surface data, and significantly broaden the class of representable shapes in the output.

Multi-target regression

LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration

no code implementations NeurIPS 2020 Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll

Formulating this closed loop is not straightforward because it is not trivial to force the output of the NN to be on the surface of the human model - outside this surface the human model is not even defined.

Self-Supervised Learning

Implicit Feature Networks for Texture Completion from Partial 3D Data

1 code implementation20 Sep 2020 Julian Chibane, Gerard Pons-Moll

Instead, we focus on 3D texture and geometry completion from partial and incomplete 3D scans.

SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing

1 code implementation ECCV 2020 Garvita Tiwari, Bharat Lal Bhatnagar, Tony Tung, Gerard Pons-Moll

SizerNet allows to estimate and visualize the dressing effect of a garment in various sizes, and ParserNet allows to edit clothing of an input mesh directly, removing the need for scan segmentation, which is a challenging problem in itself.

3D Human Pose Estimation

Unsupervised Shape and Pose Disentanglement for 3D Meshes

1 code implementation ECCV 2020 Keyang Zhou, Bharat Lal Bhatnagar, Gerard Pons-Moll

The experiments on datasets of 3D humans, faces, hands and animals demonstrate the generality of our approach.

Pose Transfer

Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction

1 code implementation ECCV 2020 Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll

In this work, we present methodology that combines detail-rich implicit functions and parametric representations in order to reconstruct 3D models of people that remain controllable and accurate even in the presence of clothing.

3D Human Pose Estimation 3D Human Reconstruction

Kinematic 3D Object Detection in Monocular Video

2 code implementations ECCV 2020 Garrick Brazil, Gerard Pons-Moll, Xiaoming Liu, Bernt Schiele

In this work, we propose a novel method for monocular video-based 3D object detection which carefully leverages kinematic motion to improve precision of 3D localization.

Ranked #8 on Monocular 3D Object Detection on KITTI Cars Moderate (using extra training data)

Monocular 3D Object Detection Vehicle Pose Estimation

DeepCap: Monocular Human Performance Capture Using Weak Supervision

no code implementations CVPR 2020 Marc Habermann, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, Christian Theobalt

Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality.

Pose Estimation

TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style

4 code implementations CVPR 2020 Chaitanya Patel, Zhouyingcheng Liao, Gerard Pons-Moll

While the low-frequency component is predicted from pose, shape and style parameters with an MLP, the high-frequency component is predicted with a mixture of shape-style specific pose models.

3D Human Pose Estimation 3D Shape Reconstruction

Learning to Transfer Texture from Clothing Images to 3D Humans

1 code implementation CVPR 2020 Aymen Mir, Thiemo Alldieck, Gerard Pons-Moll

In this paper, we present a simple yet effective method to automatically transfer textures of clothing images (front and back) to 3D garments worn on top SMPL, in real time.

Image-to-Image Translation Translation +1

Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion

1 code implementation CVPR 2020 Julian Chibane, Thiemo Alldieck, Gerard Pons-Moll

To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans.

3D Object Reconstruction 3D Reconstruction +1

NASA: Neural Articulated Shape Approximation

no code implementations6 Dec 2019 Boyang Deng, JP Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, Andrea Tagliasacchi

Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics.

360-Degree Textures of People in Clothing from a Single Image

no code implementations20 Aug 2019 Verica Lazova, Eldar Insafutdinov, Gerard Pons-Moll

In order to learn our model in a common UV-space, we non-rigidly register the SMPL model to thousands of 3D scans, effectively encoding textures and geometries as images in correspondence.

Image-to-Image Translation Translation

Multi-Garment Net: Learning to Dress 3D People from Images

6 code implementations ICCV 2019 Bharat Lal Bhatnagar, Garvita Tiwari, Christian Theobalt, Gerard Pons-Moll

We present Multi-Garment Network (MGN), a method to predict body shape and clothing, layered on top of the SMPL model from a few frames (1-8) of a video.

3D Human Pose Estimation 3D Shape Reconstruction From A Single 2D Image

XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera

4 code implementations1 Jul 2019 Dushyant Mehta, Oleksandr Sotnychenko, Franziska Mueller, Weipeng Xu, Mohamed Elgharib, Pascal Fua, Hans-Peter Seidel, Helge Rhodin, Gerard Pons-Moll, Christian Theobalt

The first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals. We contribute a new architecture for this CNN, called SelecSLS Net, that uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.

Monocular 3D Human Pose Estimation Motion Capture

Body Shape Privacy in Images: Understanding Privacy and Preventing Automatic Shape Extraction

no code implementations27 May 2019 Hosnieh Sattar, Katharina Krombholz, Gerard Pons-Moll, Mario Fritz

Modern approaches to pose and body shape estimation have recently achieved strong performance even under challenging real-world conditions.

Recommendation Systems Virtual Try-on

Tex2Shape: Detailed Full Human Body Geometry From a Single Image

1 code implementation ICCV 2019 Thiemo Alldieck, Gerard Pons-Moll, Christian Theobalt, Marcus Magnor

From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing.

Image-to-Image Translation Translation

AMASS: Archive of Motion Capture as Surface Shapes

2 code implementations ICCV 2019 Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, Michael J. Black

We achieve this using a new method, MoSh++, that converts mocap data into realistic 3D human meshes represented by a rigged body model; here we use SMPL [doi:10. 1145/2816795. 2818013], which is widely used and provides a standard skeletal representation as well as a fully rigged surface mesh.

Motion Capture

SimulCap : Single-View Human Performance Capture with Cloth Simulation

no code implementations CVPR 2019 Tao Yu, Zerong Zheng, Yuan Zhong, Jianhui Zhao, Qionghai Dai, Gerard Pons-Moll, Yebin Liu

This paper proposes a new method for live free-viewpoint human performance capture with dynamic details (e. g., cloth wrinkles) using a single RGBD camera.

Learning to Reconstruct People in Clothing from a Single RGB Camera

1 code implementation CVPR 2019 Thiemo Alldieck, Marcus Magnor, Bharat Lal Bhatnagar, Christian Theobalt, Gerard Pons-Moll

We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm.

Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera

no code implementations ECCV 2018 Timo von Marcard, Roberto Henschel, Michael J. Black, Bodo Rosenhahn, Gerard Pons-Moll

In this work, we propose a method that combines a single hand-held camera and a set of Inertial Measurement Units (IMUs) attached at the body limbs to estimate accurate 3D poses in the wild.

3D Pose Estimation

Detailed Human Avatars from Monocular Video

1 code implementation3 Aug 2018 Thiemo Alldieck, Marcus Magnor, Weipeng Xu, Christian Theobalt, Gerard Pons-Moll

We present a novel method for high detail-preserving human avatar creation from monocular video.

Fashion is Taking Shape: Understanding Clothing Preference Based on Body Shape From Online Sources

no code implementations9 Jul 2018 Hosnieh Sattar, Gerard Pons-Moll, Mario Fritz

To study the correlation between clothing garments and body shape, we collected a new dataset (Fashion Takes Shape), which includes images of users with clothing category annotations.

DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor

no code implementations CVPR 2018 Tao Yu, Zerong Zheng, Kaiwen Guo, Jianhui Zhao, Qionghai Dai, Hao Li, Gerard Pons-Moll, Yebin Liu

We further propose a joint motion tracking method based on the double layer representation to enable robust and fast motion tracking performance.

Video Based Reconstruction of 3D People Models

1 code implementation CVPR 2018 Thiemo Alldieck, Marcus Magnor, Weipeng Xu, Christian Theobalt, Gerard Pons-Moll

This paper describes how to obtain accurate 3D body models and texture of arbitrary people from a single, monocular video in which a person is moving.

3D Reconstruction Virtual Try-on

Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB

6 code implementations9 Dec 2017 Dushyant Mehta, Oleksandr Sotnychenko, Franziska Mueller, Weipeng Xu, Srinath Sridhar, Gerard Pons-Moll, Christian Theobalt

Our approach uses novel occlusion-robust pose-maps (ORPM) which enable full body pose inference even under strong partial occlusions by other people and objects in the scene.

3D Pose Estimation

Dynamic FAUST: Registering Human Bodies in Motion

no code implementations CVPR 2017 Federica Bogo, Javier Romero, Gerard Pons-Moll, Michael J. Black

We propose a new mesh registration method that uses both 3D geometry and texture information to register all scans in a sequence to a common reference topology.

A Generative Model of People in Clothing

1 code implementation ICCV 2017 Christoph Lassner, Gerard Pons-Moll, Peter V. Gehler

We present the first image-based generative model of people in clothing for the full body.

Semantic Segmentation

Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

no code implementations23 Mar 2017 Timo von Marcard, Bodo Rosenhahn, Michael J. Black, Gerard Pons-Moll

We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body.

3D Human Pose Estimation Motion Capture

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