title: Noise Calibration: Plug-and-play Content-Preserving Video Enhancement
    using Pre-trained Video Diffusion Models
summary: In order to improve the quality of synthesized videos, currently, one
  predominant method involves retraining an expert diffusion model and then
  implementing a noising-denoising process for refinement. Despite the
  significant training costs, maintaining consistency of content between the
  original and enhanced videos remains a major challenge. To tackle this
  challenge, we propose a novel formulation that considers both visual quality
  and consistency of content. Consistency of content is ensured by a proposed
  loss function that maintains the structure of the input, while visual quality
  is improved by utilizing the denoising process of pretrained diffusion models.
  To address the formulated optimization problem, we have developed a
  plug-and-play noise optimization strategy, referred to as Noise Calibration. By
  refining the initial random noise through a few iterations, the content of
  original video can be largely preserved, and the enhancement effect
  demonstrates a notable improvement. Extensive experiments have demonstrated the
  effectiveness of the proposed method.
paper_authors._id: 6695e7d1496ec7b160331ead
paper_authors.name: Qinyu Yang
paper_authors._id: 6695e7d1496ec7b160331eae
paper_authors.name: Haoxin Chen
paper_authors._id: 6695e7d1496ec7b160331eaf
paper_authors.name: Yong Zhang
paper_authors._id: 6695e7d1496ec7b160331eb0
paper_authors.name: Menghan Xia
paper_authors._id: 6695e7d1496ec7b160331eb1
paper_authors.name: Xiaodong Cun
paper_authors._id: 6695e7d1496ec7b160331eb2
paper_authors.name: Zhixun Su
paper_authors._id: 6695e7d1496ec7b160331eb3
paper_authors.name: Ying Shan
paper_title: Noise Calibration: Plug-and-play Content-Preserving Video Enhancement
    using Pre-trained Video Diffusion Models
paper_summary: In order to improve the quality of synthesized videos, currently, one
  predominant method involves retraining an expert diffusion model and then
  implementing a noising-denoising process for refinement. Despite the
  significant training costs, maintaining consistency of content between the
  original and enhanced videos remains a major challenge. To tackle this
  challenge, we propose a novel formulation that considers both visual quality
  and consistency of content. Consistency of content is ensured by a proposed
  loss function that maintains the structure of the input, while visual quality
  is improved by utilizing the denoising process of pretrained diffusion models.
  To address the formulated optimization problem, we have developed a
  plug-and-play noise optimization strategy, referred to as Noise Calibration. By
  refining the initial random noise through a few iterations, the content of
  original video can be largely preserved, and the enhancement effect
  demonstrates a notable improvement. Extensive experiments have demonstrated the
  effectiveness of the proposed method.
paper_ai_summary: A new method improves video synthesis by using noise calibration to maintain content consistency while leveraging the denoising capabilities of pretrained diffusion models.
paper_ai_keywords: diffusion models noising-denoising process loss function noise optimization Noise Calibration