HAP: A Hand-Driven Active Perception Framework for Egocentric Head Motion Prediction
cs.CV, cs.RO
Submitted: 2026-09-16
Updated: 2026-09-16
Project page: https://hap-ego.github.io/HAP
Terminology
Sources
- Robot Learning from Human Videos: A Survey
- LIME: Learning Intent-aware Camera Motion from Egocentric Video
- Act, Sense, Act: Learning Active Perception from Large-Scale Egocentric Human Data
- TAVIS: A Benchmark for Egocentric Active Vision and Anticipatory Gaze in Imitation Learning
- Towards Exploratory and Focused Manipulation with Bimanual Active Perception: A New Problem, Benchmark and Strategy
- SAM 3D: 3Dfy Anything in Images
- YOLOv11: An Overview of the Key Architectural Enhancements
- Deep Kalman Filters
Related papers
- Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
- AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
- Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
- MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
- TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
- A Survey on Efficient Vision-Language-Action Models