Text-to-motion (T2M) generation has broad applications in character animation, virtual avatars, and human-robot interaction. Existing methods typically generate pose trajectories or motion tokens directly from language, forcing a single model to handle semantic interpretation, long-horizon structure, and low-level physical realization. This coupling makes them costly and often unreliable for long, compositional, or semantically dense prompts. We propose Text2BFM, the first framework that aligns natural language with pretrained Behavioral Foundation Models (BFMs) for T2M generation without relying on heavy end-to-end motion generators. Text2BFM operates in the latent policy space of a frozen BFM, using it as an executable motion prior. A text-aligned variational behavioral bottleneck compresses BFM policy-latent sequences into compact motion representations that are compatible with language and preserve long-horizon behavioral structure. Generation is performed in this compact behavioral manifold with a lightweight conditional generator, and the resulting latent encoded behaviors are decoded into policy latents that drive the pretrained frozen BFM. By decoupling semantic planning from motion execution, Text2BFM achieves efficient, robust T2M generation and strong performance on long, compositional textual descriptions.

翻译：摘要：文本生成动作（Text-to-Motion，T2M）技术在角色动画、虚拟化身和人机交互中具有广泛应用。现有方法通常直接从语言生成姿态轨迹或动作标记，迫使单一模型同时处理语义理解、长程结构和底层物理实现。这种耦合使得模型昂贵且往往不可靠，尤其面对长序列、复合型或语义密集的提示词时。我们提出Text2BFM，这是首个将自然语言与预训练行为基础模型（BFM）对齐用于T2M生成，且无需依赖重型端到端动作生成器的框架。Text2BFM在冻结BFM的潜在策略空间中运作，将其作为可执行动作先验。一个文本对齐的变分行为瓶颈将BFM策略潜在序列压缩为与语言兼容、保留长程行为结构的紧凑动作表示。在此紧凑行为流形上，通过轻量级条件生成器执行生成过程，所得潜在编码行为被解码为驱动预训练冻结BFM的策略潜在向量。通过解耦语义规划与动作执行，Text2BFM实现了高效、鲁棒的T2M生成，并在长序列、复合型文本描述上展现出优越性能。