AI RESEARCH
From Diffusion To Flow: Efficient Motion Generation In MotionGPT3
arXiv CS.LG
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ArXi:2603.26747v1 Announce Type: cross 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 nstrated 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.