Existing video analysis models often lack explainability, perform poorly on long videos, and frequently hallucinate. Commercial solutions are closed-source and costly. We introduce CReLeRI, an open-source system for action detection in untrimmed videos. CReLeRI segments videos using scene and action transitions, detects actions and their arguments and grounds them in 3D space to improve interpretability and reduce hallucinations. The system promotes transparency and trust in AI-driven analysis of complex, real-world videos. A demonstration video is also available.

Augmented Reality (AR) interactions feature users interacting with virtual objects registered in the physical world. With contemporary AR experiences increasingly featuring interactions at distances, we conceptualized The Force, a technique that allows users to clone distant objects and manipulate their replicas. An empirical evaluation was conducted, comparing it against two well-established techniques including controller-based ray-casting and a gaze-based pinching technique in a pick-and-place task. We employed a within-subjects design, collecting data on both objective performance and subjective user experience. Results suggest that The Force allows for higher levels of accuracy and efficiency in medium-field tasks that require precision and fine motor control. Furthermore, we discovered avenues towards iteratively refining this technique. We go on to discuss the implications of our findings in an effort to facilitate better interactions in augmented reality.