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

TL;DR

Being-M0.5 is a real-time, controllable vision-language-motion model that advances human motion generation by enabling fine-grained, diverse, and long-term sequence control, supported by a large-scale dataset and novel motion tokenization.

Contribution

It introduces Being-M0.5, the first real-time controllable VLMM with a new part-aware motion tokenization technique and a comprehensive large-scale dataset, HuMo100M.

Findings

Achieves state-of-the-art performance on multiple motion benchmarks.

Demonstrates real-time generation capabilities.

Provides detailed analysis and insights for future motion generation development.

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.