Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models

5 Steps

1. 1

   Analyze Current Model Limitations: Evaluate your existing video world models for failure modes in dynamic environments, specifically when subjects temporarily leave the frame or become occluded. Identify instances of freezing, vanishing, or distortion upon re-emergence.

2. 2

   Define Hybrid Memory Requirements: Outline the functional specifications for a memory system that can robustly track objects even when they are not directly observable. Consider requirements for short-term (visible) and long-term (occluded) state management, and how to associate re-emerging objects with their past states.

3. 3

   Design Memory Architecture: Propose an architecture for a 'Hybrid Memory' component. This might involve a combination of explicit object tracking for visible entities and a more persistent, abstract representation for objects that have gone out of sight, along with mechanisms for re-identification.

4. 4

   Integrate with World Model: Determine how the designed Hybrid Memory will interact with your world model's perception, prediction, and latent state components. Map out the data flow for storing and retrieving object information to maintain world consistency across occlusions.

5. 5

   Develop Dynamic Evaluation Metrics: Establish specific metrics to assess the improved model's performance in dynamic scenarios. Focus on evaluating object persistence, re-identification accuracy, and simulation consistency when objects re-emerge after being out of sight.