Motion generation is essential for animating virtual characters and embodied
agents. While recent text-driven methods have made significant strides, they
often struggle with achieving precise alignment between linguistic descriptions
and motion semantics, as well as with the inefficiencies of slow, multi-step
inference. To address these issues, we introduce TMR++ Aligned Preference
Optimization (TAPO), an innovative framework that aligns subtle motion
variations with textual modifiers and incorporates iterative adjustments to
reinforce semantic grounding. To further enable real-time synthesis, we propose
MotionFLUX, a high-speed generation framework based on deterministic rectified
flow matching. Unlike traditional diffusion models, which require hundreds of
denoising steps, MotionFLUX constructs optimal transport paths between noise
distributions and motion spaces, facilitating real-time synthesis. The
linearized probability paths reduce the need for multi-step sampling typical of
sequential methods, significantly accelerating inference time without
sacrificing motion quality. Experimental results demonstrate that, together,
TAPO and MotionFLUX form a unified system that outperforms state-of-the-art
approaches in both semantic consistency and motion quality, while also
accelerating generation speed. The code and pretrained models will be released.