arXiv Machine Learning
Aug 19

From Diffusion to Flow: Efficient Motion Generation in MotionGPT3

The paper compares diffusion and rectified flow objectives within the MotionGPT3 framework for text-driven motion generation. Experiments on HumanML3D show that rectified flow converges faster, achieves strong test performance earlier, and matches or exceeds diffusion quality while requiring fewer sampling steps. The study isolates the generative objective’s impact, demonstrating that rectified flow’s benefits transfer to continuous-latent motion generation.

By Jaymin Bhan, JiHong Jeon, SangYeop Jeong