HY-Motion 1.0 Introduces Billion-Parameter Diffusion Transformer for Text-Driven 3D Human Motion Generation

Global: HY-Motion 1.0 Introduces Billion-Parameter Diffusion Transformer for Text-Driven 3D Human Motion Generation

In a recent arXiv preprint, researchers announced the release of HY-Motion 1.0, a large‑scale model capable of generating three‑dimensional human motions from textual descriptions. The model leverages a Diffusion Transformer architecture scaled to the billion‑parameter range, marking the first successful application of such scale within the motion‑generation domain. According to the authors, the system is designed to follow instructions with a level of fidelity that surpasses existing open‑source benchmarks.

Model Architecture and Scale

HY-Motion 1.0 builds on the Diffusion Transformer (DiT) framework, extending it to a parameter count exceeding one billion. This architectural choice enables the model to capture complex motion dynamics while maintaining the flexibility required for text‑conditioned synthesis. The authors emphasize that the scale contributes directly to the model’s ability to generalize across diverse motion categories.

Comprehensive Training Paradigm

The development process incorporates a three‑stage training pipeline. First, the model undergoes large‑scale pretraining on more than 3,000 hours of motion data. Next, a high‑quality fine‑tuning phase refines the system using 400 hours of curated recordings. Finally, reinforcement learning from human feedback and reward models aligns the generated motions with textual instructions, enhancing both relevance and realism.

Data Processing and Curation

To support the training regimen, the team implemented a rigorous data‑processing pipeline that includes motion cleaning and automated captioning. This pipeline ensures that the input dataset is both noise‑free and richly annotated, facilitating effective learning across the model’s extensive parameter space.

Performance and Coverage

According to the abstract, HY-Motion 1.0 achieves coverage of over 200 motion categories distributed among six major classes. The authors claim that this breadth, combined with the model’s instruction‑following capabilities, results in motion quality that exceeds current open‑source standards.

Open‑Source Release and Community Impact

The researchers have made HY-Motion 1.0 publicly available, inviting the broader community to explore and extend the technology. By releasing the model and its associated training scripts, the team aims to accelerate research and promote the transition of 3D human motion generation toward commercial viability.

Future Directions

The authors suggest that ongoing work will focus on expanding the dataset, refining reinforcement‑learning strategies, and exploring downstream applications such as virtual reality, gaming, and animation pipelines. They anticipate that continued community involvement will drive further improvements in both model performance and usability.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.