A Deployment Study of Identity-Gated Drone Gesture Control
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2602. 23694v3 Announce Type: replace-cross Abstract: Human operators are still frequently exposed to hazardous environments such as disaster zones and industrial facilities, where intuitive and reliable teleoperation of mobile robots and Unmanned Aerial Vehicles (UAVs) is essential.
Perceiving human motion and intent at long range is a prerequisite for socially intelligent aerial robots, yet the data to learn it barely exists. We introduce Drones2BodyLanguage, a dataset grounding human motion in real UAV footage: avatars manifesting ten communicative intents are placed into unmodified 4K drone scenes with metrically correct position, scale and orientation, maintained over hundreds of frames of camera motion.
arXiv:2608.16081v2 Announce Type: replace Abstract: Open-weight and frontier vision-language models (VLMs) perform well on general image understanding, but their ability to interpret fine-grained han...
arXiv:2607. 01435v1 Announce Type: cross Abstract: Significant advancement of immersive technologies such as Virtual and Augmented Reality (VR/AR) and their integration into diverse aspects of modern life need authentication interfaces that are secure, intuitive, and compatible with embodied interaction.
arXiv:2607. 14675v1 Announce Type: cross Abstract: Robust human-robot interaction in complex environments requires accurate gesture perception, semantic scene understanding, and reliable task planning under limited onboard computing resources.
PHOSA introduces MVSign, the first multi‑view Chinese sign language dataset co‑designed with Deaf experts, featuring diverse gestures and rich annotations. The authors develop a hybrid fitting pipeline for accurate SMPL‑X annotation and propose a decoupled sign avatar representation that isolates body, head, and hand components, coupled with a motion‑aware sampling strategy to handle motion blur and balance gesture diversity. Experiments show high‑fidelity visual results on MVSign, especially in detailed hand and facial regions, and good generalization to in‑the‑wild monocular sign language videos.