Echo-Infinity: Learning Evolving Memory for Real-Time Infinite Video Generation
We present Echo Infinity, an autoregressive (AR) framework towards real-time infinite video generation that employs a learnable evolving memory to dynamically filter, abstract, and compress any-length history at constant cost. Existing methods mainly curate memory with predefined KV-cache schedules, fixed-ratio heuristic compression, or inference-time RoPE adaptation. These designs inevitably lose historical information and amplify compounding errors due to their limited cache window and ignorance of autoregressive generation noise. Inspired by human memory consolidation, Echo-Infinity replaces handcrafted memory curation with learnable Memory Query, which are updated by attention and a g...
| Authors | Yuxuan Bian, Zeyue Xue, Songchun Zhang et al. |
|---|---|
| Primary category | cs.MM |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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