一种统一的条件流用于运动生成、编辑和内部结构重定向
A Unified Conditional Flow for Motion Generation, Editing, and Intra-Structural Retargeting
AI总结：
本文提出一种统一的条件流框架，用于运动生成、编辑和内部结构重定向，通过条件传输实现文本驱动的运动编辑和结构重定向，提升结构一致性。
摘要：
文本驱动的运动编辑和内部结构重定向，其中源和目标共享拓扑但骨长可能不同，传统上通过碎片化管道处理，输入和表示不兼容：编辑依赖于专门的生成引导，而重定向则推迟到几何后处理。我们提出一种统一的视角，将这两个任务视为条件传输在单一生成框架中的实例。通过利用最近的流匹配进展，我们证明编辑和重定向本质上是相同的生成任务，仅在推理过程中调节条件信号（语义或结构）而不同。我们通过一个联合条件化的矩形流运动模型实现这一愿景，该模型在文本提示和目标骨骼结构上进行条件化。我们的架构扩展了DiT风格的Transformer，采用每关节标记化和显式关节自注意力机制，严格 enforcing 运动学依赖。同时，多条件分类器自由引导策略平衡文本一致性和骨骼一致性。在SnapMoGen和多角色Mixamo子集上的实验表明，单个训练模型支持文本到运动生成、零样本编辑和零样本内部结构重定向。这种统一的方法简化了部署，并相比任务特定基线提高了结构一致性。
英文摘要：
Text-driven motion editing and intra-structural retargeting, where skeletons share topology but may differ in bone lengths and rest pose, are traditionally handled by fragmented pipelines with incompatible inputs and representations: editing relies on specialized generative steering, while retargeting is deferred to geometric post-processing. We present a unified conditional-flow framework that casts generation, semantic editing, and intra-structural retargeting as condition-modulated transport within one text- and skeleton-conditioned rectified-flow model. Under this formulation, editing changes the semantic condition while preserving skeletal structure, whereas retargeting changes the skeletal condition while preserving motion semantics. This makes FlowEdit-style transport a unified inference rule for motion manipulation rather than a task-specific editor. To instantiate this for articulated 3D motion, we develop a text- and skeleton-conditioned rectified-flow transformer. The model uses per-joint tokenization and explicit joint self-attention to capture spatial kinematic dependencies. We further inject text conditions at both joint and frame levels, while residual multi-condition classifier-free guidance balances text adherence and skeletal conformity. Experiments on SnapMoGen and a multi-character Mixamo subset show that one trained model supports text-to-motion generation, zero-shot editing, and zero-shot intra-structural retargeting without task-specific fine-tuning. This unified framework replaces separate pipelines with a single conditional motion transport model while keeping the same-topology retargeting scope explicit.
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