Human Object Interaction
Human-object interaction (HOI) research focuses on understanding and modeling how humans interact with objects in images and videos, aiming to accurately detect, classify, and even generate these interactions. Current research emphasizes developing robust models, often leveraging transformer architectures and diffusion models, to handle challenges like occlusion, diverse object categories, and limited training data, particularly in zero-shot and few-shot learning scenarios. This field is crucial for advancing computer vision, robotics, and human-computer interaction, with applications ranging from improved activity recognition and virtual/augmented reality to more intuitive human-robot collaboration and assistive technologies. The development of large-scale, high-quality datasets with detailed annotations is also a significant area of focus.
Papers
Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks
Haonan Chen, Yilong Niu, Kaiwen Hong, Shuijing Liu, Yixuan Wang, Yunzhu Li, Katherine Driggs-Campbell
HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs
Esteve Valls Mascaro, Daniel Sliwowski, Dongheui Lee