NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing (2406.06523v2)

Abstract: We propose a video editing framework, NaRCan, which integrates a hybrid deformation field and diffusion prior to generate high-quality natural canonical images to represent the input video. Our approach utilizes homography to model global motion and employs multi-layer perceptrons (MLPs) to capture local residual deformations, enhancing the model's ability to handle complex video dynamics. By introducing a diffusion prior from the early stages of training, our model ensures that the generated images retain a high-quality natural appearance, making the produced canonical images suitable for various downstream tasks in video editing, a capability not achieved by current canonical-based methods. Furthermore, we incorporate low-rank adaptation (LoRA) fine-tuning and introduce a noise and diffusion prior update scheduling technique that accelerates the training process by 14 times. Extensive experimental results show that our method outperforms existing approaches in various video editing tasks and produces coherent and high-quality edited video sequences. See our project page for video results at https://koi953215.github.io/NaRCan_page/.

• The paper introduces a hybrid deformation field that combines homography for global motion with MLP-based local residuals to enhance canonical image quality.
• The integration of a fine-tuned diffusion prior reduces training time by 14x while significantly improving temporal consistency and naturalness.
• The model segments complex scenes into multiple canonical images using linear interpolation, ensuring seamless transitions for versatile video editing.

NaRCan: A Hybrid Deformation Field and Diffusion Prior Framework for Video Editing

This paper presents NaRCan, a sophisticated video editing framework that integrates hybrid deformation fields and diffusion priors to enhance the quality of canonical images, which are essential for video editing tasks. The proposed methodology leverages multi-layer perceptrons (MLPs) and homography transformations to model global and local residual deformations, respectively. The introduction of diffusion prior and LoRA fine-tuning in the training process ensures high-quality, natural canonical images, outperforming existing methods in both temporal consistency and editing versatility.

Contributions

The paper's contributions are as follows:

1. Hybrid Deformation Field: The proposed model employs a hybrid deformation field combining homography estimated global motion and MLP-based local residual deformations. This combination allows the model to better capture complex video dynamics, providing a substantial advantage over canonical-based models that rely solely on MLPs.
2. Diffusion Prior Integration: By incorporating a fine-tuned diffusion prior in the initial training stages, the model ensures the generation of high-quality, natural canonical images suitable for various downstream tasks. A noise and diffusion prior update scheduling technique accelerates this process, reducing the training time by a factor of 14.
3. Separated NaRCan: For complex scenes, the model segments the video into multiple sections, creating dedicated canonical images for each. A linear interpolation strategy ensures temporal consistency across segmented canonical images, thus maintaining high-quality output in diverse scenarios.

Methodology

Hybrid Deformation Field: The model first uses homography to capture global motion, followed by an MLP to account for local residual deformations, represented as: $u',\ v' = H(u,\ v,\ t) + g(u,\ v,\ t),$

$[R, G, B] = f(u', v').$

This hybrid approach addresses the limitations of previous methods by better fitting both local and global deformations, resulting in more natural canonical images.

Diffusion Prior: By fine-tuning a latent diffusion model specifically for the video and introducing a hierarchical noise scheduling technique, the model adapts better to the specific scene. The noise and update scheduling are crucial for maintaining the fidelity and naturalness of the canonical images throughout the training: $$\mathrm{Noise~Intensity} = 0.4 \text{ (initial)}, 0.3 \text{ (mid)}, 0.2 \text{ (late)}.$$

$$\mathrm{Update~Frequency} = 10 \text{ (initial)}, 100 \text{ (mid)}, 2000 \text{ (late)}.$$

Separated NaRCan: For videos with substantial variability, the segmentation into multiple canonical images helps maintain high fidelity. Each segment overlaps slightly with adjacent ones, and frames within this region are linearly interpolated: $$\mathrm{Frame}_{t} = (1 - \alpha) \times C_{i} + \alpha \times C_{i+1},$$ where $\alpha$ gradually shifts from 0 to 1 within the overlap window, ensuring smooth transitions between segments.

Experimental Results

Temporal Consistency: The paper highlights that NaRCan achieves superior temporal consistency compared to existing methods such as CoDeF and Hashing-nvd, as evidenced by warping and interpolation errors on the BalanceCC dataset: $\mathrm{Warping~Error} = 0.364$

$\mathrm{Interpolation~Error} = 8.365$

Editing Tasks: The framework shows significant improvements in various video editing tasks, including style transfer, dynamic segmentation, and handwriting-based video edits. The use of high-quality canonical images enhances the framework's capability to maintain temporal consistency and fidelity across these diverse tasks.

User Studies

User studies further corroborate the framework's effectiveness in dimensions of temporal consistency, prompt alignment, and overall quality. Participants consistently rated NaRCan's outputs higher compared to those generated by other contemporary methods, thus validating the architecture's practical relevance and utility.

Implications and Future Directions

The implications of NaRCan are profound for both practical applications and theoretical advancements. Practically, the framework can significantly improve workflows in film production, advertising, and virtual reality by providing high-quality, temporally consistent video edits. Theoretically, the hybrid deformation model opens up new research avenues in representation learning for dynamic scenes, and the diffusion prior integration offers a robust approach for enhancing visual quality in generative models.

Future developments could focus on optimizing the LoRA fine-tuning process to further reduce computational time and cost. Additionally, incorporating more sophisticated segmentation techniques could enhance the performance of Separated NaRCan, ensuring the model remains effective even in more complex or rapidly changing scenes.

This paper establishes a robust foundation for natural, high-quality video editing by seamlessly integrating hybrid deformation models and diffusion priors. The methodologies and results presented offer a valuable resource for future innovations in video processing and generative modeling.