MemRoPE: Training-Free Infinite Video Generation via Evolving Memory Tokens

Autoregressive diffusion enables real-time frame streaming,yet existing sliding-window caches discard past context, causing fidelitydegradation, identity drift, and motion stagnation over long horizons.Current approaches preserve a fixed set of early tokens as attentionsinks, but this static anchor cannot reflect the evolving content of agrowing video. We introduce MemRoPE, a training-free framework withtwo co-designed components. Memory Tokens continuously compress allpast keys into dual long-term and short-term streams via exponentialmoving averages, maintaining both global identity and recent dynamicswithin a fixed-size cache. Online RoPE Indexing caches unrotated keysand applies positional embeddings dynamically at attention time, ensur-ing the aggregation is free of conflicting positional phases. These twomechanisms are mutually enabling: positional decoupling makes tempo-ral aggregation well-defined, while aggregation makes fixed-size cachingviable for unbounded generation. Extensive experiments validate thatMemRoPE outperforms existing methods in temporal coherence, visualfidelity, and subject consistency across minute- to hour-scale generation.