Synthetic Video Enhances Physical Fidelity in Video Synthesis (2503.20822v1)

Abstract: We investigate how to enhance the physical fidelity of video generation models by leveraging synthetic videos derived from computer graphics pipelines. These rendered videos respect real-world physics, such as maintaining 3D consistency, and serve as a valuable resource that can potentially improve video generation models. To harness this potential, we propose a solution that curates and integrates synthetic data while introducing a method to transfer its physical realism to the model, significantly reducing unwanted artifacts. Through experiments on three representative tasks emphasizing physical consistency, we demonstrate its efficacy in enhancing physical fidelity. While our model still lacks a deep understanding of physics, our work offers one of the first empirical demonstrations that synthetic video enhances physical fidelity in video synthesis. Website: https://kevinz8866.github.io/simulation/

Enhancing Physical Fidelity in Video Synthesis with Synthetic Video Data

The paper "Synthetic Video Enhances Physical Fidelity in Video Synthesis" investigates the role of synthetic video data in improving the physical realism of video generation models. This research endeavors to bridge the gap between synthetic and real-world data within the context of video synthesis, primarily leveraging computer-generated imagery (CGI) pipelines such as Blender and Unreal Engine. These CGI platforms allow for the creation of synthetic videos that adhere to physical laws, thereby providing a valuable resource to enhance the physical fidelity of generative models.

Key Contributions

1. Data Synthesis Pipeline: The authors implemented a sophisticated synthesis pipeline using CGI techniques. This pipeline allows for the generation of synthetic videos with a high degree of control over scene parameters such as lighting, camera angles, and object configurations. The generated data set emphasizes maintaining 3D consistency, a critical feature in achieving high physical fidelity.
2. Integration of Synthetic Data: A primary challenge tackled by this research is the integration of synthetic data with real-world video data within generative models. The authors propose a method to effectively transfer physical realism from synthetic videos to improve the model's consistency in respecting physical laws, while minimizing artifacts that often accompany such cross-domain data amalgamations.
3. SimDrop: A Novel Training Approach: The paper introduces SimDrop, an innovative training strategy where a secondary, synthetic-reference model is trained alongside the principal generation model. This auxiliary model captures the visual artifacts of synthetic videos, allowing the primary model to retain focus on physical fidelity without inheriting unwanted artifacts.
4. Evaluation Strategies: To assess the physical fidelity imparted by synthetic data, the research employs quantitative metrics including 3D consistency measured by reconstruction errors and human pose estimation confidence. These metrics provide insights into the improved physical realism of videos post-training.

Experimental Validation

The research validates its hypothesis through a series of comprehensive experiments across several video generation tasks, such as large human motion, wide-angle camera rotation, and video layer decomposition. The improved models, augmented with synthetic video data, consistently outperform baseline models. Specifically:

• Large human motions are generated with significantly reduced collapse and distortion, evidenced by superior pose estimation scores.
• The model adeptly learns the concept of camera motion, maintaining 3D consistency better than existing commercial models, as shown by enhanced reconstruction metrics.
• In video layer decomposition, the model successfully separates foreground objects on uniform backgrounds, a task where conventional models struggle.

Implications and Future Directions

The implications of this research are especially significant within domains requiring high fidelity video synthesis, such as virtual reality, film production, and autonomous simulation environments. By addressing the physical fidelity gap between synthetic and real data, the techniques proposed can lead to more realistic video synthesis, essential for applications reliant on precise physical interactions and realism.

Future research could explore extending synthetic data synthesis to include more complex dynamic interactions and physical effects, such as fluid dynamics or multi-object simulations. Additionally, leveraging a broader array of rendering outputs (e.g., depth, normals) as supervisory signals could further enhance model performance and generalization capabilities.

The paper makes a compelling case for the utility of synthetic video data, setting a precedent for its use in video generation tasks where physical fidelity is paramount. The methods and results presented offer a practical step forward in integrating CGI-generated content with real-world video synthesis, encouraging further exploration and application in broader AI domains.