This article previews a talk by Gül Varol from Ecole des Ponts ParisTech on bridging natural language and 3D human motions. The talk will cover text-to-motion synthesis using generative models and text-to-motion retrieval models based on the ACTOR, TEMOS, TMR, TEACH, and SINC papers. Varol's research interests include video representation learning, human motion synthesis, and sign languages. Why it matters: Research in this area could enable more intuitive human-computer interaction and new applications in areas like virtual reality and robotics.
Researchers created a cross-cultural corpus of annotated verbal and nonverbal behaviors in receptionist interactions. The corpus includes native speakers of American English and Arabic role-playing scenarios at university reception desks in Doha, Qatar, and Pittsburgh, USA. The manually annotated nonverbal behaviors include gaze direction, hand gestures, torso positions, and facial expressions. Why it matters: This resource can be valuable for the human-robot interaction community, especially for building culturally aware AI systems.
A new benchmark, ViMUL-Bench, is introduced to evaluate video LLMs across 14 languages, including Arabic, with a focus on cultural inclusivity. The benchmark includes 8k manually verified samples across 15 categories and varying video durations. A multilingual video LLM, ViMUL, is also presented, along with a training set of 1.2 million samples, with both to be publicly released.
KAUST is hosting the Marine Megafauna Movement Workshop (October 19-20) featuring international speakers showcasing research on marine animal behavior using sensors and analytics. Enrichment in the Fall 2015 (October 16-24) at KAUST will focus on marine animal movement with lectures, trips, movies, and music. KAUST aims to merge research on marine animal movement with the study of human mobility to gain new insights. Why it matters: This interdisciplinary approach could advance understanding of both marine ecosystems and human behavior, while promoting marine conservation efforts in the Red Sea.
Researchers at NYU Abu Dhabi have developed an AI system capable of translating spoken language into sign language. This innovative technology aims to enhance communication accessibility for individuals who are deaf or hard-of-hearing. The system leverages advancements in artificial intelligence, likely combining natural language processing for speech understanding and computer vision for sign generation. Why it matters: This development has the potential to significantly improve inclusion and communication for deaf communities within the Middle East and globally, bridging critical communication gaps.