SubjectSwitch: Background-Preserving Subject Switching in Interactive Long Video Generation

When a new text prompt requests a subject change during interactive long video generation, the video generation model must replace the existing subject while preserving background context that remains compatible with the new instruction. Yet, existing autoregressive models struggle with this balance, since they commonly use a key–value (KV) cache to reuse representations of previously generated frames. To address these limitations, we introduce SubjectSwitch, a training-free framework that selectively updates the recent KV cache and controls the influence of the persistent attention sink. For the recent KV cache, SubjectSwitch first identifies subject changes between consecutive prompts. It then suppresses background responses in subject attention by subtracting background attention, thereby locating the subject-related regions of the KV cache. These regions are selectively reconstructed under the new prompt, while the background regions of the KV cache are preserved. However, selectively updating the recent cache alone is insufficient to prevent subject leakage; the persistent attention sink still continuously broadcasts visual cues from the initial frames, causing the obsolete subject to reappear later in the video. To resolve this, we estimate its influence at selected generation blocks and denoising timesteps and selectively correct predictions in the affected regions, reducing the reappearance of initial-frame content in later frames. We evaluate 60-second generation with six sequential prompts on VidProM-Switch and MotionBind-Switch. SubjectSwitch achieves the best results among the compared methods on Subject Switch Cleanliness, Clean Switch Rate, and Switch Latency Score, improving Clean Switch Rate over LongLive, the strongest baseline on this metric, by 23.43 and 26.60 percentage points, respectively, while keeping the five reported VBench-Long scores within 1.3 points of LongLive.