no code implementations • ICCV 2023 • Shashank Tripathi, Agniv Chatterjee, Jean-Claude Passy, Hongwei Yi, Dimitrios Tzionas, Michael J. Black
In contrast, we focus on inferring dense, 3D contact between the full body surface and objects in arbitrary images.
no code implementations • 24 Aug 2023 • Sai Kumar Dwivedi, Cordelia Schmid, Hongwei Yi, Michael J. Black, Dimitrios Tzionas
To address this, we develop POCO, a novel framework for training HPS regressors to estimate not only a 3D human body, but also their confidence, in a single feed-forward pass.
no code implementations • 22 Aug 2023 • Omid Taheri, Yi Zhou, Dimitrios Tzionas, Yang Zhou, Duygu Ceylan, Soren Pirk, Michael J. Black
In contrast, we introduce GRIP, a learning-based method that takes, as input, the 3D motion of the body and the object, and synthesizes realistic motion for both hands before, during, and after object interaction.
no code implementations • CVPR 2023 • Maria-Paola Forte, Peter Kulits, Chun-Hao Huang, Vasileios Choutas, Dimitrios Tzionas, Katherine J. Kuchenbecker, Michael J. Black
A perceptual study shows that SGNify's 3D reconstructions are significantly more comprehensible and natural than those of previous methods and are on par with the source videos.
no code implementations • CVPR 2023 • Shashank Tripathi, Lea Müller, Chun-Hao P. Huang, Omid Taheri, Michael J. Black, Dimitrios Tzionas
Inspired by biomechanics, we infer the pressure heatmap on the body, the Center of Pressure (CoP) from the heatmap, and the SMPL body's Center of Mass (CoM).
Ranked #1 on 3D Human Pose Estimation on RICH
1 code implementation • CVPR 2023 • Yixin Chen, Sai Kumar Dwivedi, Michael J. Black, Dimitrios Tzionas
To build HOT, we use two data sources: (1) We use the PROX dataset of 3D human meshes moving in 3D scenes, and automatically annotate 2D image areas for contact via 3D mesh proximity and projection.
1 code implementation • CVPR 2023 • Yuliang Xiu, Jinlong Yang, Xu Cao, Dimitrios Tzionas, Michael J. Black
To increase robustness for these cases, existing work uses an explicit parametric body model to constrain surface reconstruction, but this limits the recovery of free-form surfaces such as loose clothing that deviates from the body.
1 code implementation • 25 Oct 2022 • Ahmed A. A. Osman, Timo Bolkart, Dimitrios Tzionas, Michael J. Black
Using novel 4D scans of feet, we train a model with an extended kinematic tree that captures the range of motion of the toes.
no code implementations • 26 Sep 2022 • Yinghao Huang, Omid Tehari, Michael J. Black, Dimitrios Tzionas
With this method we capture the InterCap dataset, which contains 10 subjects (5 males and 5 females) interacting with 10 objects of various sizes and affordances, including contact with the hands or feet.
1 code implementation • CVPR 2022 • Vasileios Choutas, Lea Muller, Chun-Hao P. Huang, Siyu Tang, Dimitrios Tzionas, Michael J. Black
Since paired data with images and 3D body shape are rare, we exploit two sources of information: (1) we collect internet images of diverse "fashion" models together with a small set of anthropometric measurements; (2) we collect linguistic shape attributes for a wide range of 3D body meshes and the model images.
Ranked #6 on 3D Human Shape Estimation on SSP-3D
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.
no code implementations • 7 Jan 2022 • Javier Romero, Dimitrios Tzionas, Michael J. Black
We attach MANO to a standard parameterized 3D body shape model (SMPL), resulting in a fully articulated body and hand model (SMPL+H).
1 code implementation • CVPR 2022 • Omid Taheri, Vasileios Choutas, Michael J. Black, Dimitrios Tzionas
This is challenging, as it requires the avatar to walk towards the object with foot-ground contact, orient the head towards it, reach out, and grasp it with a realistic hand pose and hand-object contact.
2 code implementations • CVPR 2022 • Yuliang Xiu, Jinlong Yang, Dimitrios Tzionas, Michael J. Black
First, ICON infers detailed clothed-human normals (front/back) conditioned on the SMPL(-X) normals.
Ranked #1 on 3D Human Reconstruction on CAPE
1 code implementation • 11 May 2021 • Yao Feng, Vasileios Choutas, Timo Bolkart, Dimitrios Tzionas, Michael J. Black
Second, human shape is highly correlated with gender, but existing work ignores this.
1 code implementation • CVPR 2021 • Mohamed Hassan, Partha Ghosh, Joachim Tesch, Dimitrios Tzionas, Michael J. Black
Second, we show that POSA's learned representation of body-scene interaction supports monocular human pose estimation that is consistent with a 3D scene, improving on the state of the art.
2 code implementations • ECCV 2020 • Omid Taheri, Nima Ghorbani, Michael J. Black, Dimitrios Tzionas
Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time.
1 code implementation • ECCV 2020 • Vasileios Choutas, Georgios Pavlakos, Timo Bolkart, Dimitrios Tzionas, Michael J. Black
To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image.
2 code implementations • 24 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.
1 code implementation • ICCV 2019 • Mohamed Hassan, Vasileios Choutas, Dimitrios Tzionas, Michael J. Black
To motivate this, we show that current 3D human pose estimation methods produce results that are not consistent with the 3D scene.
2 code implementations • 2 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.
1 code implementation • CVPR 2019 • Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed A. A. Osman, Dimitrios Tzionas, Michael J. Black
We use the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild.
Ranked #1 on 3D Human Reconstruction on Expressive hands and faces dataset (EHF) (TR V2V (mm), left hand metric)
3 code implementations • CVPR 2019 • Yana Hasson, Gül Varol, Dimitrios Tzionas, Igor Kalevatykh, Michael J. Black, Ivan Laptev, Cordelia Schmid
Previous work has made significant progress towards reconstruction of hand poses and object shapes in isolation.
Ranked #7 on hand-object pose on DexYCB
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.
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.
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.
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.