Particulate: Feed-Forward 3D Object Articulation
📰 ArXiv cs.AI
Particulate is a feed-forward model that infers 3D object articulations using a transformer network
Action Steps
- Train a transformer network on a diverse collection of articulated 3D assets
- Use the trained network to predict 3D parts, kinematic structure, and motion constraints of an object
- Apply the inferred articulations to various applications such as robotics, animation, and 3D modeling
- Evaluate and refine the model using metrics such as accuracy and robustness
Who Needs to Know This
Computer vision engineers and researchers on a team can benefit from this model as it enables the inference of 3D object articulations, which can be applied to various fields such as robotics and animation. This can also be useful for product managers and designers who work with 3D models
Key Insight
💡 Particulate uses a transformer network to infer 3D object articulations, enabling various applications in computer vision and robotics
Share This
🤖 Introducing Particulate: a feed-forward model for 3D object articulation inference!
Key Takeaways
Particulate is a feed-forward model that infers 3D object articulations using a transformer network
Full Article
Title: Particulate: Feed-Forward 3D Object Articulation
Abstract:
arXiv:2512.11798v2 Announce Type: replace-cross Abstract: We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion constraints. The model is based on a transformer network, the Part Articulation Transformer, which predicts all these parameters for all joints. We train the network end-to-end on a diverse collection of articulated 3D assets from public datasets. During inference, P
Abstract:
arXiv:2512.11798v2 Announce Type: replace-cross Abstract: We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion constraints. The model is based on a transformer network, the Part Articulation Transformer, which predicts all these parameters for all joints. We train the network end-to-end on a diverse collection of articulated 3D assets from public datasets. During inference, P
DeepCamp AI