Human Object Spatial Relation

Human object spatial relation research focuses on enabling machines to understand and model how humans interact with objects in 3D space, a crucial step towards embodied AI. Current efforts concentrate on learning 3D human-object relationships from 2D images, employing techniques like normalizing flows and leveraging large language models (LLMs) to interpret instructions and reason about spatial arrangements. This involves developing robust methods to handle the inherent uncertainty and variability in real-world interactions, often using novel datasets and benchmark tasks to evaluate progress. Advances in this area are vital for improving robotic manipulation, scene understanding, and human-computer interaction.