Adobe Research Unlocking Long-Term Memory in Video World Models with State-Space Models
Adobe Research publishes a method combining state-space models with dense local attention to enable long-term memory in video generation models.
By combining State-Space Models (SSMs) for efficient long-range dependency modeling with dense local attention for coherence, and using training strategies like diffusion forcing and frame local attention, researchers from Adobe Research successfully overcome the long-standing challenge of long-term memory in video generation.
Adobe Research Unlocking Long-Term Memory in Video World Models with State-Space Models first appeared on Synced.