17 citations · 49 across the 19 of their papers we have counts for
22 papers
EgoExoMoCap: Distributed Ego-Exo Human Motion Capture
Jiaxi Jiang, Bharat Lal Bhatnagar, Nan Yang +6
Human motion capture from head-mounted devices (HMDs) offers a scalable way to acquire real-world human motion and interaction data, which is crucial for applications in embodied A…
Stitched Embeddings: A Unified Latent Space for 3D Garments and 2D Patterns
Andrea Sanchietti, Riccardo Marin, Bharat Lal Bhatnagar +2
While garments are essential for realistic digital humans, their topological variety makes them much harder to model than parametric bodies. Traditional tailoring relies on 2D sewi…
Masked Modeling for Human Motion Recovery Under Occlusions
Zhiyin Qian, Siwei Zhang, Bharat Lal Bhatnagar +2
Human motion reconstruction from monocular videos is a fundamental challenge in computer vision, with broad applications in AR/VR, robotics, and digital content creation, but remai…
GEARS: Local Geometry-aware Hand-object Interaction Synthesis
Keyang Zhou, Bharat Lal Bhatnagar, Jan Eric Lenssen +1
Generating realistic hand motion sequences in interaction with objects has gained increasing attention with the growing interest in digital humans. Prior work has illustrated the e…
FORCE: Physics-aware Human-object Interaction
Xiaohan Zhang, Bharat Lal Bhatnagar, Sebastian Starke +5
Interactions between human and objects are influenced not only by the object's pose and shape, but also by physical attributes such as object mass and surface friction. They introd…
RHOBIN Challenge: Reconstruction of Human Object Interaction
Xianghui Xie, Xi Wang, Nikos Athanasiou +17
Modeling the interaction between humans and objects has been an emerging research direction in recent years. Capturing human-object interaction is however a very challenging task d…
RoHM: Robust Human Motion Reconstruction via Diffusion
Siwei Zhang, Bharat Lal Bhatnagar, Yuanlu Xu +4
We propose RoHM, an approach for robust 3D human motion reconstruction from monocular RGB(-D) videos in the presence of noise and occlusions. Most previous approaches either train…
Template Free Reconstruction of Human-object Interaction with Procedural Interaction Generation
Xianghui Xie, Bharat Lal Bhatnagar, Jan Eric Lenssen +1
Reconstructing human-object interaction in 3D from a single RGB image is a challenging task and existing data driven methods do not generalize beyond the objects present in the car…
GAN-Avatar: Controllable Personalized GAN-based Human Head Avatar
Berna Kabadayi, Wojciech Zielonka, Bharat Lal Bhatnagar +2
Digital humans and, especially, 3D facial avatars have raised a lot of attention in the past years, as they are the backbone of several applications like immersive telepresence in…
NSF: Neural Surface Fields for Human Modeling from Monocular Depth
Yuxuan Xue, Bharat Lal Bhatnagar, Riccardo Marin +4
Obtaining personalized 3D animatable avatars from a monocular camera has several real world applications in gaming, virtual try-on, animation, and VR/XR, etc. However, it is very c…
Visibility Aware Human-Object Interaction Tracking from Single RGB Camera
Xianghui Xie, Bharat Lal Bhatnagar, Gerard Pons-Moll
Capturing the interactions between humans and their environment in 3D is important for many applications in robotics, graphics, and vision. Recent works to reconstruct the 3D human…
TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement
Keyang Zhou, Bharat Lal Bhatnagar, Jan Eric Lenssen +1
We present TOCH, a method for refining incorrect 3D hand-object interaction sequences using a data prior. Existing hand trackers, especially those that rely on very few cameras, of…
COUCH: Towards Controllable Human-Chair Interactions
Xiaohan Zhang, Bharat Lal Bhatnagar, Vladimir Guzov +2
Humans interact with an object in many different ways by making contact at different locations, creating a highly complex motion space that can be difficult to learn, particularly…
BEHAVE: Dataset and Method for Tracking Human Object Interactions
Bharat Lal Bhatnagar, Xianghui Xie, Ilya A. Petrov +3
Modelling interactions between humans and objects in natural environments is central to many applications including gaming, virtual and mixed reality, as well as human behavior ana…
CHORE: Contact, Human and Object REconstruction from a single RGB image
Xianghui Xie, Bharat Lal Bhatnagar, Gerard Pons-Moll
Most prior works in perceiving 3D humans from images reason human in isolation without their surroundings. However, humans are constantly interacting with the surrounding objects,…
Adjoint Rigid Transform Network: Task-conditioned Alignment of 3D Shapes
Keyang Zhou, Bharat Lal Bhatnagar, Bernt Schiele +1
Most learning methods for 3D data (point clouds, meshes) suffer significant performance drops when the data is not carefully aligned to a canonical orientation. Aligning real world…
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration
Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt +1
We address the problem of fitting 3D human models to 3D scans of dressed humans. Classical methods optimize both the data-to-model correspondences and the human model parameters (p…
SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing
Garvita Tiwari, Bharat Lal Bhatnagar, Tony Tung +1
While models of 3D clothing learned from real data exist, no method can predict clothing deformation as a function of garment size. In this paper, we introduce SizerNet to predict…
Unsupervised Shape and Pose Disentanglement for 3D Meshes
Keyang Zhou, Bharat Lal Bhatnagar, Gerard Pons-Moll
Parametric models of humans, faces, hands and animals have been widely used for a range of tasks such as image-based reconstruction, shape correspondence estimation, and animation.…
Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction
Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt +1
Implicit functions represented as deep learning approximations are powerful for reconstructing 3D surfaces. However, they can only produce static surfaces that are not controllable…
Multi-Garment Net: Learning to Dress 3D People from Images
Bharat Lal Bhatnagar, Garvita Tiwari, Christian Theobalt +1
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. Several experiments demons…
Learning to Reconstruct People in Clothing from a Single RGB Camera
Thiemo Alldieck, Marcus Magnor, Bharat Lal Bhatnagar +2
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 w…