6 papers
ECHO: Ego-Centric modeling of Human-Object interactions
Ilya A. Petrov, Vladimir Guzov, Riccardo Marin +5
Modeling human-object interactions (HOI) from an egocentric perspective is a critical yet challenging task, particularly when relying on sparse signals from wearable devices like s…
SCENIC: Scene-aware Semantic Navigation with Instruction-guided Control
Xiaohan Zhang, Sebastian Starke, Vladimir Guzov +3
Synthesizing natural human motion that adapts to complex environments while allowing creative control remains a fundamental challenge in motion synthesis. Existing models often fal…
Blendify -- Python rendering framework for Blender
Vladimir Guzov, Ilya A. Petrov, Gerard Pons-Moll
With the rapid growth of the volume of research fields like computer vision and computer graphics, researchers require effective and user-friendly rendering tools to visualize resu…
EgoLM: Multi-Modal Language Model of Egocentric Motions
Fangzhou Hong, Vladimir Guzov, Hyo Jin Kim +4
As the prevalence of wearable devices, learning egocentric motions becomes essential to develop contextual AI. In this work, we present EgoLM, a versatile framework that tracks and…
HMD^2: Environment-aware Motion Generation from Single Egocentric Head-Mounted Device
Vladimir Guzov, Yifeng Jiang, Fangzhou Hong +5
This paper investigates the generation of realistic full-body human motion using a single head-mounted device with an outward-facing color camera and the ability to perform visual…
Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild
Lingni Ma, Yuting Ye, Fangzhou Hong +14
We introduce Nymeria - a large-scale, diverse, richly annotated human motion dataset collected in the wild with multiple multimodal egocentric devices. The dataset comes with a) fu…