Author(s): Gowtham Boyina

Originally published on Towards AI.

And It’s the First to Scale DiT This Far

I’ve watched text-to-motion generation struggle with the same problem for years: models either understand prompts decently but generate stiff, unnatural movement, or they produce smooth motion but can’t follow complex instructions. The trade-off has been frustrating — you get one or the other, rarely both.

Tencent’s release of HY-Motion 1.0 introduces a significant advancement in text-to-motion generation by utilizing a billion-parameter Diffusion Transformer (DiT) model that excels in both motion quality and instruction following. The architecture employs a unique three-stage training process to enhance performance, where initial broad training on extensive datasets is followed by fine-tuning and reinforcement learning from human feedback. This allows the model to generate naturalistic 3D animations, revolutionizing applications in game development, film production, and AI companions, while also highlighting hardware limitations and focusing on humanoid characters exclusively.

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Published via Towards AI