让视频里的物体听你摆布，还能换风格
现在的AI生成视频，如果你说「让这只猫跳舞」，它要么把猫的样子原封不动搬过去（但背景、风格改不了），要么为了换风格把猫也改得不像了。这篇论文把「保留物体本身特征」和「自由改变其他属性」拆成两件事分开处理：用Domain-MoT模块让模型知道哪些是物体的核心特征（比如猫的花纹），哪些是无关的（比如背景、画风），再用Video-Reference DualRoPE把参考图和视频放在不同的空间里，互不干扰。结果就是：你可以让同一只猫出现在水墨画里、赛博朋克街头、或者变成毛绒玩具，猫还是那只猫，但环境随便换。它不是明天就能用的产品，但给视频生成加上了「指哪打哪」的精确控制——做广告、动画、短视频的人，这是你们以后调AI素材的方向。
📄 原文摘要(英文)
Open domain subject-driven text-to-video (S2V) generation has drawn significant interest in academia and industry. Open domain S2V mainly involves two scenarios: in-domain, which requires retaining the reference subject features as much as possible, and cross-domain, which preserves the intrinsic features of the subject while allowing subject-irrelevant properties to vary flexibly according to the text prompt. Existing methods primarily focus on maximizing subject fidelity in in-domain scenarios, which limits their editability and adaptability in cross-domain scenarios, such as novel styles, semantic combinations, or domain attributes. In this study, we propose that an ideal S2V method should flexibly shuttle between different domains, achieving strong performance in both in-domain and cross-domain scenarios. To this end, we propose DomainShuttle, which could achieve high fidelity and generative flexibility for open domain video personalization. Specifically, we introduce Domain-MoT, which decouples videos and reference features and introduces the domain-aware AdaLN for domain-specific modeling of reference images. We then introduce the Video-Reference DualRoPE scheme, which places reference image tokens and video tokens in separate RoPE spaces to enable precise subject-level spatial modeling, and Cross-Pair Consistent Loss, which aims to extract intrinsic subject features unaffected by irrelevant features. Extensive experiments demonstrate that DomainShuttle achieves significant performance improvements over existing methods, exhibiting high subject fidelity and generative flexibility across diverse open domain application scenarios.