Rectified Flow Outperforms Diffusion in MotionGPT3 for Text-to-Motion Generation

Researchers have conducted a study comparing diffusion and rectified flow objectives within the MotionGPT3 framework for text-driven motion generation. The experiments, performed on the HumanML3D dataset, indicate that rectified flow converges faster, achieves strong performance earlier, and matches or surpasses diffusion-based motion quality under identical conditions. Furthermore, flow-based priors offer better efficiency-quality trade-offs with fewer sampling steps, suggesting that the benefits of rectified flow extend to continuous-latent text-to-motion generation. AI

IMPACT Highlights the potential for improved efficiency and quality in text-to-motion generation models.

RANK_REASON The cluster contains an academic paper detailing a comparative study of generative objectives for motion generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• HumanML3D
• Jaymin Bhan
• MotionGPT3

• paper
• model release

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