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Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models (2407.15642v2)

Published 22 Jul 2024 in cs.CV

Abstract: Diffusion models have achieved great progress in image animation due to powerful generative capabilities. However, maintaining spatio-temporal consistency with detailed information from the input static image over time (e.g., style, background, and object of the input static image) and ensuring smoothness in animated video narratives guided by textual prompts still remains challenging. In this paper, we introduce Cinemo, a novel image animation approach towards achieving better motion controllability, as well as stronger temporal consistency and smoothness. In general, we propose three effective strategies at the training and inference stages of Cinemo to accomplish our goal. At the training stage, Cinemo focuses on learning the distribution of motion residuals, rather than directly predicting subsequent via a motion diffusion model. Additionally, a structural similarity index-based strategy is proposed to enable Cinemo to have better controllability of motion intensity. At the inference stage, a noise refinement technique based on discrete cosine transformation is introduced to mitigate sudden motion changes. Such three strategies enable Cinemo to produce highly consistent, smooth, and motion-controllable results. Compared to previous methods, Cinemo offers simpler and more precise user controllability. Extensive experiments against several state-of-the-art methods, including both commercial tools and research approaches, across multiple metrics, demonstrate the effectiveness and superiority of our proposed approach.

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Citations (2)
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Summary

  • The paper introduces a motion diffusion approach that learns motion residuals to generate smooth, temporally consistent animated videos.
  • It employs SSIM-based motion intensity control to align generated motion with textual prompts for precise video dynamics.
  • DCTInit refines noise using low-frequency image components, significantly reducing sudden motion artifacts and enhancing video quality.

Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models

Introduction

The field of image-to-video (I2V) generation, or image animation, has been a longstanding challenge within the computer vision community. The core objective of I2V generation is to create video sequences from static images that exhibit natural dynamics while preserving the detailed information of the original image. This process has important applications in photography, filmmaking, and augmented reality. Despite significant advancements made by previous methods, maintaining spatio-temporal consistency and ensuring smooth transitions guided by textual prompts has remained a challenge.

In the paper titled "Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models," the authors introduce a novel approach to address these issues. The proposed model, Cinemo, aims to achieve superior motion controllability and stronger temporal consistency and smoothness.

Key Contributions

The paper highlights three primary contributions:

  1. Motion Residual Learning: Cinemo deviates from traditional methods that predict subsequent video frames directly. Instead, it learns the distribution of motion residuals, effectively guiding the model to generate motion dynamics that are both smooth and consistent with the input image.
  2. Motion Intensity Control: A Structural Similarity Index (SSIM)-based strategy provides fine-grained control over the intensity of motion in the generated videos. This technique allows for better alignment between the generated video and the input textual prompt, without incurring significant computational costs.
  3. Noise Refinement Using Discrete Cosine Transform (DCT): To mitigate sudden motion changes during the inference phase, the authors introduce DCTInit. This method refines the noise input using low-frequency components extracted from the input image, enabling the model to handle discrepancies between training and inference phases effectively.

Methodology

Motion Residual Learning

Cinemo's architecture leverages a foundational text-to-video (T2V) diffusion model, specifically LaVie. During training, Cinemo learns the distribution of motion residuals by incorporating appearance information from the input static image. This technique ensures that the model generates motion patterns while preserving the consistency of the input static image across frames.

SSIM-Based Motion Intensity Control

The authors propose a novel strategy that uses the SSIM to control video motion intensity. By calculating the SSIM between consecutive frames and incorporating it as a condition during training, Cinemo can produce videos with varying degrees of motion intensity that align closely with the input parameters.

DCTInit for Noise Refinement

To address the discrepancies between training and inference noise, Cinemo employs DCTInit. This method utilizes the low-frequency components of the input image's Discrete Cosine Transform to refine the inference noise, leading to smoother and more temporally consistent video generation. The choice of DCT over FFT ensures better handling of color consistency issues, which are critical for realistic video generation.

Experimental Results

The authors validate Cinemo's performance on several metrics, including Fréchet Video Distance (FVD), Inception Score (IS), Fréchet Inception Distance (FID), and CLIP similarity (CLIPSIM). The results show that Cinemo achieves state-of-the-art performance across various datasets, outperforming existing methods both qualitatively and quantitatively. Notably, Cinemo demonstrates superior image consistency and motion controllability, essential for generating high-quality animated videos.

Practical Implications

The robust performance and versatility of Cinemo have significant implications for practical applications. The ability to generate consistent, smooth, and controllable animated videos from static images can substantially enhance user experiences in diverse fields such as digital content creation, virtual reality, and augmented reality. Additionally, Cinemo's approach can be extended to video editing and motion transfer tasks, showcasing its adaptability to various video generation applications.

Future Directions

The paper suggests several potential future developments:

  1. Scaling with Transformers: Given the trend towards Transformer-based architectures in video generation, Cinemo's principles could be further validated and optimized using models like Latte.
  2. Resolution Enhancement: Improving the resolution of generated videos beyond the current limit could further enhance the model's applicability in high-definition content creation.
  3. Integration with Real-World Applications: Implementing Cinemo in practical tools and commercial products could bridge the gap between research and real-world usage, providing valuable insights for future improvements.

Conclusion

Cinemo introduces a novel and effective approach to image animation by focusing on motion residual learning and integrating innovative strategies for motion intensity control and noise refinement. The model's ability to produce highly consistent, smooth, and controllable animated videos represents a significant step forward in the field of I2V generation. The extensive quantitative and qualitative experiments demonstrate Cinemo's superiority over existing methods, paving the way for future advancements in AI-driven video generation.

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