Generating realistic humanoid motion from scene images and text involves both low-frequency pose semantics and high-frequency physical dynamics. However, many existing methods tokenize motion with a single shared codebook, forcing heterogeneous motion signals into the same quantization space. Our frequency-domain analysis of human motion data reveals a clear mismatch between single-codebook quantization and motion statistics: five DCT coefficients capture 93% of joint-position energy but only 37% of joint-velocity energy, which can bias quantization toward pose statistics and under-represent high-frequency velocity components. A second challenge lies in adapting a standard autoregressive model to effectively model high-frequency physical signals in motion sequences. Therefore, we propose DSFT, a dual-stream frequency tokenizer that separates motion into Base and physical streams and compresses them independently with DCT truncation and BPE. Furthermore, we present MotionVLA, a Qwen3.5-based model that arranges Base and physical tokens in a unified sequence, where Phys tokens are predicted after Base tokens. Experiments on HumanML3D and MBench show that, despite using a lightweight 2B backbone, MotionVLA reduces the Diversity gap to real data by over 50% on HumanML3D and improves Motion-Condition Consistency by 3.8% on MBench, supporting frequency-aware dual-stream decoupling as an effective formulation for autoregressive motion generation. Code: https://github.com/AIGeeksGroup/MotionVLA. Website: https://aigeeksgroup.github.io/MotionVLA.

翻译：从场景图像与文本生成逼真的人形运动既涉及低频的姿态语义，也包含高频的物理动力学。然而，现有许多方法采用单一共享码本对运动进行分词，将异质的运动信号强制压缩至同一量化空间。我们对人体运动数据的频域分析揭示了单码本量化与运动统计数据之间的明显失配：五个离散余弦变换系数捕获了关节位置能量的93%，却仅捕获了关节速度能量的37%，这可能导致量化偏向于姿态统计数据，从而欠表征高频速度分量。第二个挑战在于如何使标准自回归模型有效建模运动序列中的高频物理信号。为此，我们提出双流频率分词器DSFT，它将运动分离为基础流与物理流，并利用DCT截断与BPE分别对两者进行独立压缩。进一步，我们提出MotionVLA，该模型基于Qwen3.5架构，将基础令牌与物理令牌排列在统一序列中，其中物理令牌在基础令牌之后进行预测。在HumanML3D与MBench上的实验表明，尽管采用轻量级2B骨干网络，MotionVLA在HumanML3D上将运动多样性差距相对于真实数据降低了超过50%，并在MBench上将运动-条件一致性提升了3.8%，验证了频率感知的双流解耦作为自回归运动生成的有效范式。代码：https://github.com/AIGeeksGroup/MotionVLA。网站：https://aigeeksgroup.github.io/MotionVLA。