Tuning-Free Noise Rectification for High Fidelity Image-to-Video
  Generation
Weijie Li Litong Gong Yiran Zhu Fanda Fan Biao Wang Tiezheng Ge Bo Zheng
Image-to-video (I2V) generation tasks always suffer from keeping high
fidelity in the open domains. Traditional image animation techniques primarily
focus on specific domains such as faces or human poses, making them difficult
to generalize to open domains. Several recent I2V frameworks based on diffusion
models can generate dynamic content for open domain images but fail to maintain
fidelity. We found that two main factors of low fidelity are the loss of image
details and the noise prediction biases during the denoising process. To this
end, we propose an effective method that can be applied to mainstream video
diffusion models. This method achieves high fidelity based on supplementing
more precise image information and noise rectification. Specifically, given a
specified image, our method first adds noise to the input image latent to keep
more details, then denoises the noisy latent with proper rectification to
alleviate the noise prediction biases. Our method is tuning-free and
plug-and-play. The experimental results demonstrate the effectiveness of our
approach in improving the fidelity of generated videos. For more image-to-video
generated results, please refer to the project website:
https://noise-rectification.github.io.