Particulate: Feed-Forward 3D Object Articulation
Summary: arXiv:2512.11798v2 | Announce Type: replace-cross
Abstract
In the rapidly evolving field of computer vision and graphics, we introduce Particulate, a revolutionary feed-forward model designed to infer articulations of 3D objects. Given a 3D mesh, Particulate can accurately identify and predict various parameters such as 3D parts, kinematic structures, and motion constraints. The foundation of this model is the Part Articulation Transformer, a sophisticated transformer network capable of predicting all necessary parameters for each joint in a 3D mesh.
Key Features of Particulate
Particulate stands out due to several innovative features:
- End-to-End Training: The model is trained end-to-end on a wide range of articulated 3D assets sourced from public datasets, ensuring robustness and versatility.
- Rapid Inference: During inference, the model efficiently maps the network’s output back to the input mesh, resulting in fully articulated 3D models in a matter of seconds. This is a dramatic improvement compared to previous methods that relied on per-object optimization, which were often time-consuming.
- Compatibility with AI-Generated Assets: Particulate is also capable of processing AI-generated 3D assets. When combined with an off-the-shelf image-to-3D model, it can generate articulated 3D objects from a single real or synthetic image, broadening its application potential.
New Benchmark for 3D Articulation Estimation
To further enhance the field of 3D articulation estimation, we introduce a novel benchmark curated from high-quality public 3D assets. This benchmark not only challenges existing models but also redefines the evaluation protocol to align more closely with human preferences, ensuring that the assessments are both comprehensive and relevant.
Performance Comparison
Empirical evaluations have shown that Particulate significantly outperforms state-of-the-art approaches in various metrics. These improvements can be attributed to the model’s innovative architecture and training methodology, which leverage the strengths of transformer networks for complex 3D tasks.
Conclusion
Particulate represents a significant advancement in the field of 3D object articulation. By offering rapid, accurate predictions and the ability to work seamlessly with both real and AI-generated assets, this model paves the way for future research and applications in computer vision, gaming, and virtual reality. As the demand for realistic 3D models continues to grow, innovations like Particulate will play a crucial role in meeting these needs.