The recent advancements in the field of human hand and object interaction research are pushing the boundaries of both data collection and model capabilities. There is a notable shift towards leveraging multi-modal data, including multi-view videos and proprioceptive data, to enhance the accuracy and realism of 3D tracking and forecasting models. This trend is evident in the development of datasets that provide comprehensive ground-truth annotations, enabling more robust evaluations and advancements in tasks such as 3D hand tracking, object pose estimation, and future interaction prediction. Additionally, there is a growing emphasis on generating realistic and controllable hand images, which is crucial for applications in augmented reality and human-computer interaction. The integration of large-scale domain-specific learning and diffusion models is paving the way for more sophisticated image generation techniques that can handle the complexities of hand articulation and viewpoint variations. Furthermore, the field is witnessing innovations in instructional video generation, where models are being developed to produce detailed and contextually accurate visual instructions, particularly in egocentric views. These developments collectively indicate a move towards more immersive and interactive technologies, driven by advancements in data quality, model architecture, and computational capabilities.