Generative Physical AI in Vision: A Survey (2501.10928v2)

Abstract: Generative AI has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication. This transformation builds upon a foundation of generative models to produce realistic images, videos, and 3D/4D content. Conventional generative models primarily focus on visual fidelity while often neglecting the physical plausibility of the generated content. This gap limits their effectiveness in applications that require adherence to real-world physical laws, such as robotics, autonomous systems, and scientific simulations. As generative models evolve to increasingly integrate physical realism and dynamic simulation, their potential to function as "world simulators" expands. Therefore, the field of physics-aware generation in computer vision is rapidly growing, calling for a comprehensive survey to provide a structured analysis of current efforts. To serve this purpose, the survey presents a systematic review, categorizing methods based on how they incorporate physical knowledge, either through explicit simulation or implicit learning. It also analyzes key paradigms, discusses evaluation protocols, and identifies future research directions. By offering a comprehensive overview, this survey aims to help future developments in physically grounded generation for computer vision. The reviewed papers are summarized at https://tinyurl.com/Physics-Aware-Generation.

• The paper introduces novel paradigms like Generation to Simulation (GtS) and Simulation-Constrained Generation (ScG) to incorporate physical realism in generative models.
• It reviews simulation techniques such as the Material Point Method and Finite Element Method for accurate physical interaction modeling.
• The study underscores challenges in evaluating physical fidelity and calls for standardized, task-oriented metrics to benchmark generated content.

Overview of "Generative Physical AI in Vision: A Survey"

The paper "Generative Physical AI in Vision: A Survey" provides a comprehensive overview of the burgeoning field of physics-aware generative AI within computer vision. The authors seek to address the integration of physical realism and dynamic simulation into generative models, which traditionally focus on creating visually convincing outputs. The inclusion of physics-aware features is pivotal for applications that necessitate adherence to real-world physical laws, such as robotics and autonomous systems.

Key Contributions

The paper delineates various approaches to embedding physical awareness into generative models:

1. Generative Paradigms: It identifies several paradigms through which physical simulation is incorporated into generative models. This includes "Generation to Simulation" (GtS), where simulation follows generation, and "Simulation-Constrained Generation" (ScG), where simulation is used as a guiding constraint during generation. This categorization helps in structuring future research and systematically exploring different methodologies.
2. Physical Simulation and Understanding: It reviews simulation methods like Material Point Method (MPM) and Finite Element Method (FEM) that support dynamic simulation by modeling interactions governed by physics. Furthermore, the document distinguishes between manual, automatic, and LLM-reasoned physical parameter estimation methods, highlighting the diverse techniques employed to integrate physical realism.
3. Evaluation and Benchmarks: The survey outlines existing challenges in evaluating physical awareness, noting the absence of standardized metrics. It proposes leveraging simulators to test the physical plausibility of generated content and suggests incorporating task-oriented evaluations.
4. State-of-the-Art Models: The paper acknowledges the advancements brought by large video generation models, which implicitly gain physical reasoning capabilities due to their extensive training data. However, it flags that despite yielding impressive visual results, these models struggle with replicating physical laws accurately.

Theoretical and Practical Implications

The survey's exploration reveals that the improved integration of physics into generative models significantly enhances the model's applicability in domains requiring physical fidelity. It discusses the potential for these models to serve as foundational technologies that can simulate real-world environments more accurately for tasks in robotics, virtual reality, and scientific simulations.

Practically, the introduction of physics into generative models could revolutionize simulation practices across multiple industries by offering more realistic and interactive environments for testing and development. For instance, in robotics, these models could facilitate the transfer from simulated to real-world conditions, bridging the "simulation to reality" gap.

Future Directions

The paper also sets a roadmap for future exploration in physics-aware generative AI:

• Enhanced Evaluation Metrics: Developing rigorous, standardized metrics to evaluate the physical plausibility of generated visual content remains a priority to quantify progress in this field effectively.
• Neural-Symbolic Hybrid Models: Integrating symbolic reasoning with neural networks to enhance the interpretability and expressiveness of physical models.
• Generative Simulation Engines: Future advancements might look toward developing text-to-simulation engines capable of generating interactive, physics-grounded virtual environments from textual descriptions.
• Cross-Disciplinary Applications: By addressing the current limitations, physics-aware generative models could extend their applications into fields like healthcare and climate modeling, where high-fidelity simulation is critical.

Conclusion

The survey by Liu et al. provides a foundational perspective on advancing generative models to include physical fidelity in computer vision tasks. This research is poised to steer future studies towards more integrated, efficient, and practical applications of generative AI, fostering developments that align virtual simulations more closely with the dynamics of the physical world. The promising approaches outlined in the survey underscore the importance of interdisciplinarity in addressing complex real-world problems through AI.