Physic Grounded Vision Foundation Models for Human Computer Interaction in Embodied Environments

Physic-ground vision foundation models for human-computer interaction represent a transformative paradigm in artificial intelligence, as they extend beyond conventional data-driven perception to incorporate explicit reasoning about the physical laws and causal structures that govern the real world. Unlike earlier generations of vision models that excelled at pattern recognition but faltered when faced with tasks demanding robust predictions of dynamics, affordances, or embodied interactions, these new approaches explicitly embed principles of physics into large-scale multimodal architectures. This integration allows systems to not only recognize objects and interpret scenes but also anticipate outcomes, model constraints, and interact in ways that are coherent with the embodied experience of humans. The result is a class of foundation models that hold profound implications for applications ranging from assistive robotics and healthcare to education, design, and collaborative work, where safety, interpretability, and physical plausibility are paramount.In this review, we explore the conceptual underpinnings, methodological innovations, and broad implications of physic-ground vision foundation models. We highlight the technical advances that have made large-scale physical reasoning feasible, including differentiable physics simulators, causal representation learning, and multimodal integration strategies that combine visual, tactile, and proprioceptive inputs into unified frameworks. We also examine the computational challenges inherent in simulating high-dimensional physical dynamics, the scarcity of richly embodied datasets, and the difficulties of bridging synthetic-to-real gaps in interactive environments. Beyond technical considerations, we emphasize the interdisciplinary nature of the field, drawing on insights from cognitive science, neuroscience, robotics, and the social sciences to show how these models can be both technically robust and socially meaningful.Crucially, we discuss the ethical, philosophical, and societal implications of deploying physic-ground systems in real-world contexts. By enabling machines to act in ways that are physically grounded, these models reshape the balance of autonomy and control in human-computer interaction, raising questions of trust, accountability, equity, and inclusivity. They also invite deeper reflection on the nature of intelligence itself, as machines begin to approximate forms of embodied reasoning once considered unique to humans. Looking forward, we argue that the future of physic-ground vision foundation models depends not only on technical breakthroughs but also on interdisciplinary collaboration and human-centered design, ensuring that these systems serve as partners in creativity, learning, and problem-solving rather than opaque or paternalistic arbiters of human activity. In this way, physic-ground models embody both the extraordinary potential and the immense responsibility that defines the next era of human-computer interaction.