title: Synthetic Video Enhances Physical Fidelity in Video Synthesis
summary: 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/
paper_authors._id: 67e61017b116b3c559188a0f
paper_authors.name: Qi Zhao
paper_authors._id: 67e61017b116b3c559188a10
paper_authors.name: Xingyu Ni
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:17:44.258Z
paper_authors._id: 67b576e39b7058fa21ab72a3
paper_authors.avatarUrl: /avatars/5393231dc585950d9579323521f41ff4.svg
paper_authors.fullname: Xingyu Ni
paper_authors.type: user
paper_authors.user: Univstar
paper_authors._id: 67e61017b116b3c559188a11
paper_authors.name: Ziyu Wang
paper_authors._id: 67e61017b116b3c559188a12
paper_authors.name: Feng Cheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:18:07.030Z
paper_authors._id: 65c3dfb180497543ca257ffd
paper_authors.avatarUrl: /avatars/1eb9e114e2c4dc2dc2c3b2e3f387d214.svg
paper_authors.fullname: Feng Cheng
paper_authors.type: user
paper_authors.user: fengcheng1
paper_authors._id: 67e61017b116b3c559188a13
paper_authors.name: Ziyan Yang
paper_authors._id: 67e61017b116b3c559188a14
paper_authors.name: Lu Jiang
paper_authors._id: 67e61017b116b3c559188a15
paper_authors.name: Bohan Wang
paper_title: Synthetic Video Enhances Physical Fidelity in Video Synthesis
paper_summary: 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/
paper_ai_summary: Using synthetic videos derived from computer graphics, a method is proposed to improve physical fidelity in video generation models by reducing artifacts and enhancing consistency.
paper_ai_keywords: synthetic videos computer graphics pipelines 3D consistency physical realism video synthesis physical fidelity