6 papers · 1 filter
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…
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…
PICO: Reconstructing 3D People In Contact with Objects
Alpár Cseke, Shashank Tripathi, Sai Kumar Dwivedi +4
Recovering 3D Human-Object Interaction (HOI) from single color images is challenging due to depth ambiguities, occlusions, and the huge variation in object shape and appearance. Th…
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…
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.…