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.

翻译：图像到视频生成任务在开放域中始终面临保真度不足的挑战。传统图像动画技术主要专注于人脸或人体姿态等特定领域，难以泛化到开放域。近期基于扩散模型的图像到视频生成框架虽能为开放域图像生成动态内容，却无法保持保真度。研究发现，低保真度的两个主要因素是去噪过程中图像细节丢失和噪声预测偏差。为此，我们提出一种可应用于主流视频扩散模型的有效方法。该方法通过补充更精确的图像信息与噪声矫正实现高保真生成。具体而言，给定指定图像，我们的方法首先向输入图像潜变量添加噪声以保留更多细节，然后通过适当矫正对含噪潜变量进行去噪以缓解噪声预测偏差。本方法无需调参且即插即用。实验结果证明了该方法在提升生成视频保真度方面的有效性。更多图像到视频生成结果请参阅项目网站：https://noise-rectification.github.io。