Video Replanning via Latent Embedding Refinement and Rejection-Based Sampling
The framework integrates interaction data into video planning by updating model parameters online and filtering bad plans during generation. It uses prior videos to create state embeddings, refines these embeddings against current interactions, and employs a rejection module to select the most promising plan among candidates. This allows for dynamic adaptation in unknown environments without explicitly modeling hidden state variables.