AI拍视频终于能记住主角长啥样了
现在的AI视频生成，每换一个镜头，主角的脸、衣服、声音就可能变，像换了个人。UnityShots用两个固定大小的记忆槽——一个记住开场镜头（长期），一个记住上一镜结尾（短期），每次切换镜头时，通过一个检测画面切变和音乐节拍的阀门，决定该保留哪些信息。音频也单独注入一个参考声音token，保证说话音色不变。结果：开源模型里它跨镜头一致性最强，甚至追平了最强闭源系统。它不是你明天就能用的工具，但告诉你AI视频从“单镜头魔术”走向“多镜头电影”的关键一步：让AI学会“记住”。
📄 原文摘要(英文)
Generating a coherent multi-shot video requires structured cross-shot memory. Subject appearance, scene context, and speaker identity must persist across cuts. Existing approaches either train end-to-end over fixed-length sequences and cannot scale, generate shot-by-shot with memory banks that grow linearly, or orchestrate pretrained generators under an LLM planner without a multi-shot-aware backbone. We present UnityShots, a memory-driven multi-shot audio-video generation system built on LTX-2.3, trained on annotated cinematic and music-video shots. The video stream maintains two fixed-size slots, a long-term memory (LTM) slot anchored to the opening shot and a short-term memory (STM) slot holding the immediately preceding tail, both updated at every cut by a boundary-conditioned gate that fuses visual cut probability and beat-tracker signals. The audio stream injects a reference speaker token at every shot to preserve vocal timbre without a sliding audio bank. A discrete cut-type prior, learned through AdaLN, becomes an inference-time control knob over transition strength. We release a benchmark of 200 multi-cultural multi-shot sequences spanning six ethnic regions and ten or more languages, with per-shot reference identities, reference audio, and per-boundary transition labels. Evaluated across I2V, T2V, and R2V conditioning modes, UnityShots leads open-source baselines on every cross-shot coherence metric and matches the strongest closed-source system on the multi-shot axes.