Summary of Geometric Trajectory Diffusion Models, by Jiaqi Han et al.
Geometric Trajectory Diffusion Models
by Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel generative model called Geometric Trajectory Diffusion Models (GeoTDM) for modeling the temporal distribution of 3D geometric trajectories in various domains, such as molecule and protein design. The existing approaches only operate on static structures, neglecting the dynamic nature of physical systems. GeoTDM leverages SE(3)-equivariant spatial convolution and temporal attention to capture complex spatial interactions with physical symmetries and temporal correspondence encapsulated in the dynamics. It also introduces a generalized learnable geometric prior into the forward diffusion process for conditional generation. The model is evaluated on various scenarios, including physical simulation, molecular dynamics, and pedestrian motion, demonstrating significant improvement in generating realistic geometric trajectories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GeoTDM is a new way to create 3D models that move over time. Currently, computers can only make still images or short videos of molecules and proteins. But real-world systems are always changing! The authors created GeoTDM to fix this problem by making dynamic 3D models that capture the movement of these tiny structures. They tested it on different types of data and found that it works really well. This is important because it can help scientists design new medicines or materials. |
Keywords
» Artificial intelligence » Attention » Diffusion » Generative model
