This repository curates research at the intersection of generative modeling and physical laws, aiming to imbue AI with a deeper understanding of the physical world. It targets researchers and developers in AI, computer vision, and robotics seeking to create more scientifically accurate and predictive generative systems. The primary benefit is advancing generative AI capabilities by grounding them in fundamental physical principles, opening new avenues for scientific discovery and realistic simulation.

How It Works

The project appears to be a comprehensive collection of research papers categorized by modality and application, including image, video, 3D, and 4D generation, as well as physics simulation platforms and evaluation methods. The core approach involves integrating physics engines, simulation platforms, and physics understanding derived from observations or videos into generative models. This physics-aware methodology aims to produce outputs that adhere to physical laws, leading to more plausible and controllable generation.

Quick Start & Requirements

This repository is currently under construction and primarily serves as a curated list of research papers. It does not provide installation instructions, runnable code, or specific system requirements. Users interested in implementing physics-aware generation will need to refer to the individual research papers listed for their respective setup and dependencies.

Highlighted Details

• Extensive coverage of recent (2023-2025) research in physics-aware generation across image, video, 3D, and 4D modalities.
• Includes a dedicated section on physics engines and simulation platforms, crucial for grounding generative models.
• Features research on physics understanding from videos and observational data, enabling models to learn physical dynamics.
• Covers various physics evaluation benchmarks and methodologies for assessing generative model performance.

Maintenance & Community

Information regarding maintainers, community channels (e.g., Discord, Slack), or a project roadmap is not available in the provided README.

Licensing & Compatibility

No specific license information is provided in the README. Users should consult the individual research papers for licensing details related to their respective codebases or datasets.

Limitations & Caveats

The repository is explicitly marked as "Under Construction" and functions as a literature collection rather than a deployable project. It lacks executable code, practical examples, or guidance on integrating the listed research into a unified framework, requiring users to navigate and implement individual research papers independently.