254 citations · 275 across the 16 of their papers we have counts for
17 papers
UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors
Xiaoyan Cong, Zekun Li, Zhiyang Dou +9
Large-scale foundation models (LFMs) have recently made impressive progress in text-to-motion generation by learning strong generative priors from massive 3D human motion datasets…
NGL: Natural Garment Language for Training-Free Sewing Pattern Estimation
Anna Badalyan, Pratheba Selvaraju, Giorgio Becherini +3
Estimating sewing patterns from images is a practical approach for creating high-quality 3D garments, but it remains challenging due to the scarcity of paired real-world image and…
FUSION: Full-Body Unified Motion Prior for Body and Hands via Diffusion
Enes Duran, Nikos Athanasiou, Muhammed Kocabas +2
Hands are central to interacting with our surroundings and conveying gestures, making their inclusion essential for full-body motion synthesis. Despite this, existing human motion…
Moving by Looking: Towards Vision-Driven Avatar Motion Generation
Markos Diomataris, Berat Mert Albaba, Giorgio Becherini +3
The way we perceive the world fundamentally shapes how we move, whether it is how we navigate in a room or how we interact with other humans. Current human motion generation method…
InteractVLM: 3D Interaction Reasoning from 2D Foundational Models
Sai Kumar Dwivedi, Dimitrije Antić, Shashank Tripathi +4
We introduce InteractVLM, a novel method to estimate 3D contact points on human bodies and objects from single in-the-wild images, enabling accurate human-object joint reconstructi…
NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models
Mert Albaba, Chenhao Li, Markos Diomataris +3
Acquiring physically plausible motor skills across diverse and unconventional morphologies-including humanoid robots, quadrupeds, and animals-is essential for advancing character s…
Predicting 4D Hand Trajectory from Monocular Videos
Yufei Ye, Yao Feng, Omid Taheri +3
We present HaPTIC, an approach that infers coherent 4D hand trajectories from monocular videos. Current video-based hand pose reconstruction methods primarily focus on improving fr…
A Versatile and Differentiable Hand-Object Interaction Representation
Théo Morales, Omid Taheri, Gerard Lacey
Synthesizing accurate hands-object interactions (HOI) is critical for applications in Computer Vision, Augmented Reality (AR), and Mixed Reality (MR). Despite recent advances, the…
HUMOS: Human Motion Model Conditioned on Body Shape
Shashank Tripathi, Omid Taheri, Christoph Lassner +3
Generating realistic human motion is essential for many computer vision and graphics applications. The wide variety of human body shapes and sizes greatly impacts how people move.…
3D Whole-body Grasp Synthesis with Directional Controllability
Georgios Paschalidis, Romana Wilschut, Dimitrije Antić +2
Synthesizing 3D whole bodies that realistically grasp objects is useful for animation, mixed reality, and robotics. This is challenging, because the hands and body need to look nat…
WANDR: Intention-guided Human Motion Generation
Markos Diomataris, Nikos Athanasiou, Omid Taheri +3
Synthesizing natural human motions that enable a 3D human avatar to walk and reach for arbitrary goals in 3D space remains an unsolved problem with many applications. Existing meth…
GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistency
Omid Taheri, Yi Zhou, Dimitrios Tzionas +4
Hands are dexterous and highly versatile manipulators that are central to how humans interact with objects and their environment. Consequently, modeling realistic hand-object inter…
3D Human Pose Estimation via Intuitive Physics
Shashank Tripathi, Lea Müller, Chun-Hao P. Huang +3
Estimating 3D humans from images often produces implausible bodies that lean, float, or penetrate the floor. Such methods ignore the fact that bodies are typically supported by the…
ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation
Zicong Fan, Omid Taheri, Dimitrios Tzionas +4
Humans intuitively understand that inanimate objects do not move by themselves, but that state changes are typically caused by human manipulation (e.g., the opening of a book). Thi…
GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping
Omid Taheri, Vasileios Choutas, Michael J. Black +1
Generating digital humans that move realistically has many applications and is widely studied, but existing methods focus on the major limbs of the body, ignoring the hands and hea…
Human Leg Motion Tracking by Fusing IMUs and RGB Camera Data Using Extended Kalman Filter
Omid Taheri, Hassan Salarieh, Aria Alasty
Human motion capture is frequently used to study rehabilitation and clinical problems, as well as to provide realistic animation for the entertainment industry. IMU-based systems,…
GRAB: A Dataset of Whole-Body Human Grasping of Objects
Omid Taheri, Nima Ghorbani, Michael J. Black +1
Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shap…