Search Results for author:
Found 9 papers, 8 papers with code
Social EgoMesh Estimation
Accurately estimating the 3D pose of the camera wearer in egocentric video sequences is crucial to modeling human behavior in virtual and augmented reality applications.
TI-PREGO: Chain of Thought and In-Context Learning for Online Mistake Detection in PRocedural EGOcentric Videos
Mistakes are detected as mismatches between the currently recognized action and the action predicted by the anticipation module.
Following the Human Thread in Social Navigation
We propose the first Social Dynamics Adaptation model (SDA) based on the robot's state-action history to infer the social dynamics.
PREGO: online mistake detection in PRocedural EGOcentric videos
We propose PREGO, the first online one-class classification model for mistake detection in PRocedural EGOcentric videos.
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes.
ICML 2023 Topological Deep Learning Challenge : Design and Results
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning.
Staged Contact-Aware Global Human Motion Forecasting
So far, only Mao et al. NeurIPS'22 have addressed scene-aware global motion, cascading the prediction of future scene contact points and the global motion estimation.
Ranked #2 on
Human Pose Forecasting
on GTA-IM Dataset
About latent roles in forecasting players in team sports
Forecasting players in sports has grown in popularity due to the potential for a tactical advantage and the applicability of such research to multi-agent interaction systems.
Best Practices for 2-Body Pose Forecasting
The task of collaborative human pose forecasting stands for predicting the future poses of multiple interacting people, given those in previous frames.
