Noise Calibration: Plug-and-play Content-Preserving Video Enhancement
  using Pre-trained Video Diffusion Models
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.
6695e7d1496ec7b160331ead
Qinyu Yang
6695e7d1496ec7b160331eae
Haoxin Chen
6695e7d1496ec7b160331eaf
Yong Zhang
6695e7d1496ec7b160331eb0
Menghan Xia
6695e7d1496ec7b160331eb1
Xiaodong Cun
claimed_verified
2025-06-30T06:24:02.732Z
63184c517ca1b876d99b7e0e
https://cdn-avatars.huggingface.co/v1/production/uploads/63184c517ca1b876d99b7e0e/b-qDExoeJuDXK0cJBZKnz.jpeg
Xiaodong Cun
user
vinthony
6695e7d1496ec7b160331eb2
Zhixun Su
6695e7d1496ec7b160331eb3
Ying Shan
Noise Calibration: Plug-and-play Content-Preserving Video Enhancement
  using Pre-trained Video Diffusion Models
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.
A new method improves video synthesis by using noise calibration to maintain content consistency while leveraging the denoising capabilities of pretrained diffusion models.
diffusion models noising-denoising process loss function noise optimization Noise Calibration