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.
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
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.
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.
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.
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.
no code implementations • 1 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.
1 code implementation • 5 Apr 2022 • Xianghui Xie, Bharat Lal Bhatnagar, Gerard Pons-Moll
However, humans are constantly interacting with the surrounding objects, thus calling for models that can reason about not only the human but also the object and their interaction.
1 code implementation • CVPR 2022 • Bharat Lal Bhatnagar, Xianghui Xie, Ilya A. Petrov, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll
We present BEHAVE dataset, the first full body human- object interaction dataset with multi-view RGBD frames and corresponding 3D SMPL and object fits along with the annotated contacts between them.
no code implementations • 1 May 2022 • Xiaohan Zhang, Bharat Lal Bhatnagar, Vladimir Guzov, Sebastian Starke, Gerard Pons-Moll
In this work, we study the problem of synthesizing scene interactions conditioned on different contact positions on the object.
no code implementations • 16 May 2022 • Keyang Zhou, Bharat Lal Bhatnagar, Jan Eric Lenssen, Gerard Pons-Moll
The core of our method are TOCH fields, a novel spatio-temporal representation for modeling correspondences between hands and objects during interaction.
no code implementations • CVPR 2023 • Xianghui Xie, Bharat Lal Bhatnagar, Gerard Pons-Moll
In this work, we propose a novel method to track the 3D human, object, contacts between them, and their relative translation across frames from a single RGB camera, while being robust to heavy occlusions.
no code implementations • ICCV 2023 • Yuxuan Xue, Bharat Lal Bhatnagar, Riccardo Marin, Nikolaos Sarafianos, Yuanlu Xu, Gerard Pons-Moll, Tony Tung
Compared to existing approaches, our method eliminates the expensive per-frame surface extraction while maintaining mesh coherency, and is capable of reconstructing meshes with arbitrary resolution without retraining.
no code implementations • 22 Nov 2023 • Berna Kabadayi, Wojciech Zielonka, Bharat Lal Bhatnagar, Gerard Pons-Moll, Justus Thies
For controlling the model, we learn a mapping from 3DMM facial expression parameters to the latent space of the generative model.
no code implementations • 12 Dec 2023 • Xianghui Xie, Bharat Lal Bhatnagar, Jan Eric Lenssen, Gerard Pons-Moll
We generate 1M+ human-object interaction pairs in 3D and leverage this large-scale data to train our HDM (Hierarchical Diffusion Model), a novel method to reconstruct interacting human and unseen objects, without any templates.
no code implementations • 7 Jan 2024 • Xianghui Xie, Xi Wang, Nikos Athanasiou, Bharat Lal Bhatnagar, Chun-Hao P. Huang, Kaichun Mo, Hao Chen, Xia Jia, Zerui Zhang, Liangxian Cui, Xiao Lin, Bingqiao Qian, Jie Xiao, Wenfei Yang, Hyeongjin Nam, Daniel Sungho Jung, Kihoon Kim, Kyoung Mu Lee, Otmar Hilliges, Gerard Pons-Moll
Modeling the interaction between humans and objects has been an emerging research direction in recent years.
no code implementations • 16 Jan 2024 • Siwei Zhang, Bharat Lal Bhatnagar, Yuanlu Xu, Alexander Winkler, Petr Kadlecek, Siyu Tang, Federica Bogo
We apply RoHM to a variety of tasks -- from motion reconstruction and denoising to spatial and temporal infilling.
no code implementations • 17 Mar 2024 • Xiaohan Zhang, Bharat Lal Bhatnagar, Sebastian Starke, Ilya Petrov, Vladimir Guzov, Helisa Dhamo, Eduardo Pérez-Pellitero, Gerard Pons-Moll
Our key insight is that human motion is dictated by the interrelation between the force exerted by the human and the perceived resistance.