Limitations and Challenges

• The Reality Gap (Sim2Real Challenge): The fundamental disconnect between even the best simulations/synthetics and the complexities of the real world (physics, lighting, textures, infinite novelty). Mitigation (domain randomization, meta-learning) remains imperfect.

• Computational Intensity: Generating high-fidelity, temporally consistent visual predictions or synthetic data (especially video/3D) is extremely resource-intensive, limiting real-time application.

• Hallucination vs. Imagination: Inaccurate or incomplete world models lead to visual "hallucinations" – imagined futures or synthetic data that are physically implausible or misrepresent reality. Distinguishing robust prediction from flawed hallucination is critical and difficult.

• Bias Amplification: Visual generative models inherently learn and amplify biases in their training data. Synthetic "dreams" based on biased models perpetuate and can exacerbate these biases in agent perception and decision-making.

• Evaluating Visual Creativity & Fidelity: Quantifying the novelty, utility, and realism of AI-generated visual concepts is subjective and challenging. Hallucination detection metrics are nascent.

• The Explainability Black Box: Understanding why an agent generated a specific visual scenario or made a decision based on a visual imagination is profoundly difficult with complex deep generative models, hindering trust and debugging.

• Data Dependence: The quality of imagination/dreams is wholly dependent on the quality and breadth of the training data and the resulting model.