Adobe Research Unlocking Long-Term Memory in Video World Models with State-Space 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. The post Adobe Research Unlocking Long-Term Memory in Video World Models with State-Space Models first appeared on Synced.

📰 Original Source

Read full article at Syncedreview →

KhanList aggregates and links to publicly available news content. We do not host full articles from third-party sources. Always verify important information with original sources.