🤖 AI Summary
This study systematically compares diffusion models and rectified flows in the context of text-driven motion generation within a continuous latent space. Leveraging a unified MotionGPT3 architecture and consistent training protocols, we conduct controlled experiments on the HumanML3D dataset and demonstrate, for the first time, the advantages of rectified flows for this task. Specifically, rectified flows exhibit faster training convergence and superior early-stage generation quality, achieving motion fidelity on par with or exceeding that of diffusion models under identical conditions. Notably, they maintain stable and competitive performance even with significantly fewer sampling steps. These findings highlight the greater potential of rectified flows in balancing generation quality and computational efficiency for text-to-motion synthesis.
📝 Abstract
Recent text-driven motion generation methods span both discrete token-based approaches and continuous-latent formulations. MotionGPT3 exemplifies the latter paradigm, combining a learned continuous motion latent space with a diffusion-based prior for text-conditioned synthesis. While rectified flow objectives have recently demonstrated favorable convergence and inference-time properties relative to diffusion in image and audio generation, it remains unclear whether these advantages transfer cleanly to the motion generation setting. In this work, we conduct a controlled empirical study comparing diffusion and rectified flow objectives within the MotionGPT3 framework. By holding the model architecture, training protocol, and evaluation setup fixed, we isolate the effect of the generative objective on training dynamics, final performance, and inference efficiency. Experiments on the HumanML3D dataset show that rectified flow converges in fewer training epochs, reaches strong test performance earlier, and matches or exceeds diffusion-based motion quality under identical conditions. Moreover, flow-based priors exhibit stable behavior across a wide range of inference step counts and achieve competitive quality with fewer sampling steps, yielding improved efficiency--quality trade-offs. Overall, our results suggest that several known benefits of rectified flow objectives do extend to continuous-latent text-to-motion generation, highlighting the importance of the training objective choice in motion priors.