Omid Taheri — accepted AI-conference papers

2026

CLUTCH: Contextualized Language model for Unlocking Text-Conditioned Hand motion modelling in the wild

Balamurugan Thambiraja, Omid Taheri, Radek Danecek, Giorgio Becherini, Gerard Pons-Moll, Justus Thies

ICLR 2026poster

Hands play a central role in daily life, yet modeling natural hand motions remains underexplored. Existing methods that tackle text-to-hand-motion generation or hand animation captioning rely on studio-captured datasets with limited actions and contexts, making them costly to scale to “in-the-wild”…

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2026

NIL: No-data Imitation Learning

Mert Albaba, Chenhao Li, Markos Diomataris, Omid Taheri, Andreas Krause, Michael J. Black

CVPR 2026

Acquiring physically plausible motor skills across diverse and unconventional embodiments, including humanoids and quadrupeds, is essential for advancing character simulation and robotics. Traditional methods, such as reinforcement learning (RL), require extensive reward function engineering. Imitat

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2025

InteractVLM: 3D Interaction Reasoning from 2D Foundational Models

Sai Kumar Dwivedi, Dimitrije Antić, Shashank Tripathi, Omid Taheri, Cordelia Schmid, Michael J. Black +1

CVPR 2025poster

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 reconstruction in 3D. This is challenging due to occlusions, depth ambiguities, and widely varying object shapes. Existing methods r…

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2024

HUMOS: Human Motion Model Conditioned on Body Shape

Shashank Tripathi*, Omid Taheri, Christoph Lassner*, Michael J. Black*, Daniel Holden*, Carsten Stoll*

ECCV 2024poster

"Generating realistic human motion is crucial for many computer vision and graphics applications. The rich diversity of human body shapes and sizes significantly influences how people move. However, existing motion models typically overlook these differences, using a normalized, average body instead…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗

2024

WANDR: Intention-guided Human Motion Generation

Markos Diomataris, Nikos Athanasiou, Omid Taheri, Xi Wang, Otmar Hilliges, Michael J. Black

CVPR 2024poster

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 methods (data-driven or using reinforcement learning) are limited in terms of generalization and motion naturalness. A prima…

Cited by 12 Source ↗PDF ↗Scholar ↗

2023

3D Human Pose Estimation via Intuitive Physics

Shashank Tripathi, Lea Müller, Chun-Hao P. Huang, Omid Taheri, Michael J. Black, Dimitrios Tzionas

CVPR 2023poster

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 scene. A physics engine can be used to enforce physical plausibility, but these are not differentiable, rely on unreali…

Cited by 98 Source ↗PDF ↗Scholar ↗

2023

ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation

Zicong Fan, Omid Taheri, Dimitrios Tzionas, Muhammed Kocabas, Manuel Kaufmann, Michael J. Black +1

CVPR 2023poster

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). This is not yet the case for machines. In part this is because there exist no datasets with ground-truth 3D annotations for…

Cited by 174 Source ↗PDF ↗code ↗Scholar ↗

2022

GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping

Omid Taheri, Vasileios Choutas, Michael J. Black, Dimitrios Tzionas

CVPR 2022poster

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 head. Hands have been separately studied, but the focus has been on generating realistic static grasps of objects. To synth…

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2020

GRAB: A Dataset of Whole-Body Human Grasping of Objects

Omid Taheri, Nima Ghorbani, Michael J. Black, Dimitrios Tzionas

ECCV 2020poster

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. While ""grasping"" is commonly thought of as a single hand stably lifting an object…

Cited by 441 Source ↗PDF ↗code ↗Scholar ↗