MemActive: Training-Free Memory Activation for Consistent Interactive Streaming Video Generation

Interactive streaming video generation requires following evolving prompts while recalling entities such as characters or scenes beyond the recent context. However, frozen generators can fail to recover visual content even when relevant historical memory is supplied. We term this failure the memory-utilization gap. In a controlled experiment with fixed memory, temporal RoPE compression improves consistency with the supplied history, accompanied by an increase in memory attention mass. Motivated by this finding, we introduce MemActive, a training-free framework combining Route-Specific Query RoPE (RSQ-RoPE) with Semantic Structured Memory (SSM). RSQ-RoPE adjusts memory-route positions according to entity-description compatibility while retaining native local-route positions. SSM supplies entity-specific memory for retrieval and recombination across prompts. Quantitative and qualitative experiments under hard cuts and smooth transitions show that MemActive preserves recurring content over long temporal gaps. These consistency gains further extend to four additional training and training-free streaming backbones.