StreamReal: Reference-Guided Subject Switching in Streaming Video Generation

Reference-guided subject switching requires a causal streaming generator to adopt subjects specified by new visual references during an ongoing rollout while preserving scene and temporal continuity. This is challenging because accumulated history entangles preceding-subject information with context needed for continuation. Comparisons on an adapted causal generator show that retaining the historical cache or recaching it under updated text yields weak alignment with new references, whereas clearing it improves responsiveness over subsequent chunks at the cost of aesthetics and motion smoothness. We introduce StreamReal, which organizes causal history to distinguish subject-dependent information from context that remains relevant across reference updates. To establish reference conditioning, Few-Step Conditioning Alignment aligns a pretrained reference pathway with a frozen few-step causal generator, followed by reference-conditioned Distribution Matching Distillation warm-up to adapt the reference branch to self-generated histories. During streaming, Memory Flow maintains a cross-chunk semantic index associating historical information with the evolving subject. Upon a reference update, Memory-Guided History Routing uses this index to regulate access to native historical states, suppressing preceding-subject evidence while preserving context for coherent continuation. We introduce a Subject Switch Benchmark spanning diverse subject categories and switching positions. Evaluations on OpenS2V-Eval and this benchmark show that StreamReal combines subject fidelity and visual quality with a favorable balance between switching responsiveness and temporal continuity.