Being-M0.5: A Real-Time Controllable Vision-Language-Motion Model

• URL: http://arxiv.org/abs/2508.07863v1
• Date: Mon, 11 Aug 2025 11:26:10 GMT
• Title: Being-M0.5: A Real-Time Controllable Vision-Language-Motion Model
• Authors: Bin Cao, Sipeng Zheng, Ye Wang, Lujie Xia, Qianshan Wei, Qin Jin, Jing Liu, Zongqing Lu,
• Abstract summary: Being-M0.5 is the first real-time, controllable vision-language-motion model that achieves performance across multiple motion generation tasks.<n>Our approach is built upon HuMo100M, the largest and most comprehensive human motion dataset to date.<n>We introduce a novel part-aware residual quantization technique for motion tokenization that enables precise, granular control over individual body parts during generation.
• Score: 67.8026841949812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human motion generation has emerged as a critical technology with transformative potential for real-world applications. However, existing vision-language-motion models (VLMMs) face significant limitations that hinder their practical deployment. We identify controllability as a main bottleneck, manifesting in five key aspects: inadequate response to diverse human commands, limited pose initialization capabilities, poor performance on long-term sequences, insufficient handling of unseen scenarios, and lack of fine-grained control over individual body parts. To overcome these limitations, we present Being-M0.5, the first real-time, controllable VLMM that achieves state-of-the-art performance across multiple motion generation tasks. Our approach is built upon HuMo100M, the largest and most comprehensive human motion dataset to date, comprising over 5 million self-collected motion sequences, 100 million multi-task instructional instances, and detailed part-level annotations that address a critical gap in existing datasets. We introduce a novel part-aware residual quantization technique for motion tokenization that enables precise, granular control over individual body parts during generation. Extensive experimental validation demonstrates Being-M0.5's superior performance across diverse motion benchmarks, while comprehensive efficiency analysis confirms its real-time capabilities. Our contributions include design insights and detailed computational analysis to guide future development of practical motion generators. We believe that HuMo100M and Being-M0.5 represent significant advances that will accelerate the adoption of motion generation technologies in real-world applications. The project page is available at https://beingbeyond.github.io/Being-M0.5.

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