https://github.com/AABBCCDKG/Video_prediction_through_physical_laws

Introduction

Importance:

Predicting 2D video sequences is a crucial task in understanding and simulating real-world dynamics, which has applications in various fields, including gaming, autonomous systems, and industrial robotics.

Problem:

• Video prediction in physical systems: In physical systems, predicting object motion and interactions accurately is vital for creating realistic simulations and models. Current approaches often rely heavily on machine learning models, such as neural networks, to make predictions, but these models can fall short in specific domains where precise adherence to physical laws is necessary.
• Data scarcity and domain shift: Training neural networks for video prediction typically requires large datasets from specific domains. When shifting to a different domain, retraining the model becomes necessary, but data is often scarce or expensive, making it impractical to achieve accurate results in a timely manner.

Gap:

• Lack of physical law adherence in predictions: Many current neural network-based models do not integrate physical laws into their predictions. Instead, they rely on learned patterns, which can result in physically inaccurate outcomes, such as "clipping" or non-physical object interactions in video game designs.
• Dependence on large, domain-specific datasets: Current models need extensive retraining when moving to a new domain. This retraining process requires significant amounts of domain-specific data, which is often difficult to obtain, thereby limiting the scalability of these models.

Consequences:

• Difficulty in adapting predictive models to new domains: The need for retraining neural networks with new domain-specific data can result in time delays, high costs, and limited flexibility when applying these models in different environments. This makes them impractical for real-time or rapidly changing applications.
• Reduced applicability in industrial fields: When predictions do not follow real-world physical laws, their usefulness in industrial and scientific fields is limited. These predictions are better suited for creative fields where physical accuracy is not a priority. However, for fields like robotics or autonomous vehicles, adherence to physical laws is critical, and failures in this regard could lead to significant safety issues.

Approach:

To address these issues, we propose a framework for predicting object motion that incorporates physical laws:

1. Identifying object positions in video sequences and fitting these to position functions.
2. Calculating dynamic parameters such as acceleration and velocity from the fitted functions.
3. Simulating object interactions using a physics engine to handle phenomena like collisions.
4. Generating predicted video sequences in the form of sketches.
5. Mapping textures to predictions using cGAN, allowing for more accurate and realistic video outputs based on input video textures.