Beijing, April 7, 2025 – A team of researchers from Tsinghua University and Peking University have developed PartRM, a groundbreaking model for predicting the motion of hinged objects at a part-level resolution. This innovation, accepted to CVPR 2025, represents a significant step forward in creating general-purpose world models, particularly for robotic manipulation tasks.
Predicting the movement of hinged objects based on visual observation is crucial for building robust world models that can be used in real-world applications. While diffusion-based methods have shown promise, they often suffer from computational inefficiency and a lack of robust 3D perception, hindering their deployment in practical environments.
PartRM addresses these limitations by introducing a novel reconstruction-based approach to modeling part-level dynamics. Given a single input image and a corresponding drag action, PartRM generates a 3D representation of the object’s future state. This capability allows for the creation of data that can be directly utilized in robotic manipulation and other related tasks.
Our research focuses on overcoming the limitations of existing diffusion-based methods by leveraging a reconstruction model that provides a more efficient and accurate representation of hinged object dynamics, explains Yu Gasai, a lead researcher on the project. PartRM allows us to predict how individual parts of an object will move, providing a more granular understanding of the object’s overall motion.
The research team emphasizes the importance of this work for advancing the field of robotics. By accurately predicting the movement of hinged objects, robots can better interact with their environment and perform complex tasks with greater precision.
Experimental results demonstrate that PartRM achieves significant improvements in generating accurate and realistic predictions of hinged object motion. The model’s ability to generate 3D representations of future states makes it particularly valuable for applications such as robotic assembly, grasping, and manipulation.
The development of PartRM marks a significant advancement in the field of world modeling and offers a promising avenue for future research. The team hopes that this work will inspire further innovation in the development of more robust and efficient models for understanding and interacting with the physical world.
Key Takeaways:
- PartRM is a novel reconstruction-based model for predicting the part-level motion of hinged objects.
- It addresses the limitations of diffusion-based methods by offering improved efficiency and 3D perception.
- PartRM generates 3D representations of future object states, making it suitable for robotic manipulation tasks.
- Experimental results demonstrate significant improvements in prediction accuracy.
Further Information:
- Paper Title: PartRM: Modeling Part-Level Dynamics with Large Cross-State Reconstruction Model
- Project Page: https://partrm.c7w.tech/
- Paper Link: https://arxiv.org/abs/2503.19913
- Code Link: https://github.com/GasaiYU/PartRM
References:
- GasaiYU. (2025). PartRM: Modeling Part-Level Dynamics with Large Cross-State Reconstruction Model. Retrieved from https://arxiv.org/abs/2503.19913
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