Tencent Hunyuan’s 3D Digital Human team has introduced HY-Motion 1.0, a billion-parameter text-to-3D motion generation model built on the Diffusion Transformer (DiT) architecture with Flow Matching. This model translates natural language prompts into 3D human motion clips using a unified SMPL-H skeleton, making it suitable for digital humans, game characters, and cinematics. The model is trained on a vast dataset of over 3,000 hours of motion data, including high-quality motion capture and animation assets, and is designed to improve instruction following and motion realism through reinforcement learning techniques. HY-Motion 1.0 is available on GitHub and Hugging Face, offering developers tools and interfaces for integration into various animation and game development pipelines. Why this matters: HY-Motion 1.0 represents a significant advancement in AI-driven 3D animation, enabling more realistic and diverse character motions from simple text prompts, which can enhance digital content creation across industries.
Tencent’s release of the HY-Motion 1.0 model represents a significant advancement in the field of text-to-3D human motion generation. This billion-parameter model, built on the Diffusion Transformer (DiT) architecture and Flow Matching, allows developers to create 3D human motion clips from natural language prompts. By utilizing a unified SMPL-H skeleton, the model can generate animations that can be seamlessly integrated into various applications such as digital humans, cinematics, and interactive characters. The availability of these models on platforms like GitHub and Hugging Face, along with code and a Gradio interface, makes it accessible for developers to experiment and implement in their projects.
The training process of HY-Motion 1.0 is noteworthy due to its extensive use of diverse data sources, including human motion videos, motion capture data, and 3D animation assets. This comprehensive dataset, meticulously curated and filtered, ensures high-quality motion sequences that are retargeted onto a unified skeleton. The multi-stage filtering process eliminates anomalies and artifacts, resulting in a robust training corpus. The taxonomy developed by the research team further organizes the data into over 200 motion categories, capturing a wide range of human activities. This structured approach to data curation and taxonomy ensures that the model can handle a diverse set of motion prompts with high fidelity.
The HY-Motion 1.0 model’s architecture is a hybrid of dual stream and single stream DiT, which incorporates strong text conditioning. By using asymmetric attention and dual text encoders, the model effectively fuses token-level and global semantics into motion trajectories. This design choice enhances the model’s ability to follow instructions accurately and generate realistic motion sequences. Additionally, the use of Flow Matching instead of standard denoising diffusion provides stable training for long sequences, which is crucial for generating coherent and realistic animations. The integration of reinforcement learning techniques, such as Direct Preference Optimization and Flow GRPO, further refines the model’s performance by aligning it with semantic and physics rewards.
The significance of HY-Motion 1.0 lies in its potential to revolutionize the way developers create and implement 3D animations. By providing a tool that can accurately translate natural language prompts into detailed motion sequences, it opens up new possibilities in fields such as gaming, virtual reality, and digital content creation. The model’s ability to handle a wide array of actions and its high-quality output make it a valuable asset for developers looking to enhance their projects with realistic human motion. As the technology continues to evolve, such advancements in AI-driven animation generation are likely to have a profound impact on the digital media landscape, offering more immersive and interactive experiences for users.
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5 responses to “Tencent’s HY-Motion 1.0: Text-to-3D Motion Model”
While HY-Motion 1.0’s use of a unified SMPL-H skeleton is impressive for creating consistent 3D human motion, it would be beneficial to consider the potential limitations in capturing diverse body types and movements, which could affect the realism and inclusivity of the model’s outputs. Incorporating more varied skeletal structures or adapting the model to account for different physiologies might enhance its applicability across a broader range of characters. How does the model address or plan to address the representation of non-standard body types in its motion outputs?
The post highlights that HY-Motion 1.0 uses a unified SMPL-H skeleton, which indeed might present limitations in capturing diverse body types. While the current model focuses on consistent 3D motion, adapting it to include varied skeletal structures could enhance its inclusivity. For more detailed information on future developments, it might be best to refer to the original article linked in the post.
The post suggests that future iterations of the model could potentially address these limitations by incorporating more varied skeletal structures. For detailed insights on how the developers plan to tackle this, it’s best to refer to the original article linked in the post or contact the authors directly.
The post suggests that future iterations of the model could indeed address these limitations by incorporating more varied skeletal structures. For more detailed insights on the developers’ plans, it’s best to refer to the original article linked in the post or reach out to the authors directly.
The post suggests that while HY-Motion 1.0 currently uses a unified skeleton, future iterations could explore more diverse skeletal structures to enhance realism and inclusivity. For detailed insights into future developments, referring to the original article linked in the post might provide more comprehensive information.