MotionVLA: Vision-Language-Action Model for Humanoid Motion

2026-06-13

AI Summary

Existing methods for humanoid motion generation typically employ a single codebook to uniformly quantize both low-frequency pose semantics and high-frequency physical dynamics, which struggles to adequately capture fine-grained motion details. To address this limitation, this work proposes a Dual-Stream Frequency-domain Tokenizer (DSFT), introducing for the first time a frequency-aware decoupling mechanism: base poses are extracted via DCT truncation, while physical dynamics are compressed using Byte Pair Encoding (BPE), yielding two separate token streams. Built upon the Qwen-3.5 architecture, the MotionVLA model autoregressively predicts base tokens followed by physical tokens. Experiments demonstrate that this approach reduces the diversity gap by over 50% on HumanML3D and improves action-condition consistency by 3.8% on MBench, validating the efficacy of frequency-domain decoupling even within a lightweight 2B-parameter model.

Abstract

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.

Problem

• humanoid motion generation
• frequency-domain representation
• pose semantics
• physical dynamics
• autoregressive modeling

Innovation

• dual-stream frequency tokenizer
• DCT truncation
• autoregressive motion generation
• vision-language-action model
• frequency-aware decoupling