Synthetic Video Enhances Physical Fidelity in Video Synthesis
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Abstract (arXiv)
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/
Key facts. Cited by 15 papers in the map (data through 13 Oct 2026). Builds on 54 of its 78 references that are in the map. First posted on arXiv 26 Mar 2025.
Builds on
54 of 78 references.
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- 2025What about gravity in video generation? Post-Training Newton's Laws with Verifiable Rewardscited by 24
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- 2026Why Do Video Diffusion Models Violate Physics? Unveiling the Flaws in Attention Mechanismscited by 2
- 2025Envision: Embodied Visual Planning via Goal-Imagery Video Diffusioncited by 0
- 2026DynaVid: Learning to Generate Highly Dynamic Videos using Synthetic Motion Datacited by 0
- 2026Why Do Video Diffusion Models Violate Physics? Unveiling the Flaws in Attention Mechanisms20 Sep 2026
- 2026DynaVid: Learning to Generate Highly Dynamic Videos using Synthetic Motion Data2 Apr 2026
- 2025Envision: Embodied Visual Planning via Goal-Imagery Video Diffusion27 Dec 2025
- 2025What about gravity in video generation? Post-Training Newton's Laws with Verifiable Rewards29 Nov 2025
- 2025Enhancing Physical Plausibility in Video Generation by Reasoning the Implausibility29 Sep 2025
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- 2026HuM-Eval: A Coarse-to-Fine Framework for Human-Centric Video Evaluationcited by 0
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- 2026HuM-Eval: A Coarse-to-Fine Framework for Human-Centric Video Evaluation28 Apr 2026
- 2026PhysInOne: Visual Physics Learning and Reasoning in One Suite10 Apr 2026
- 2026AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors28 Jan 2026
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