StreamLRM: Test-Time Trained Memory for Streaming Multi-view 4D Reconstruction

Large Reconstruction Models (LRMs) have recently shown remarkable perfor- mance in feed-forward static 3D reconstruction. In this work, we explore how such powerful static models can be extended to volumetric video reconstruction, where synchronized cameras share the same multi-view geometry across time. A straightforward frame-wise extension treats each timestamp as an independent static scene, overlooking this temporal structure and repeatedly performing ex- pensive cross-view adaptation. We revisit TTT-based LRMs and reveal a key temporal asymmetry: scene content evolves with current observations, whereas fast-weight adaptation for cross-view interaction exhibits substantial temporal re- dundancy. Based on this observation, we propose StreamLRM, a simple training- free framework that temporally decouples TTT Update and Apply. We perform Update only at a reference timestamp and share the resulting fast weights across time, while each timestamp retains its own current-frame processing to capture evolving scene content. In this way, StreamLRM effectively extends pretrained static TTT-based LRMs to volumetric video without additional temporal modules or retraining. Surprisingly, this static-to-dynamic extension requires less computa- tion rather than more, achieving multi-fold inference acceleration with negligible reconstruction quality degradation on diverse multi-view video benchmarks.