In an article submitted to the ArXiv* server, researchers tackled a distinctive challenge posed by text-driven diffusion-based video editing, a challenge absent in image editing literature: the establishment of real-world motion.
Study: DreamMotion: Innovating Video Editing through Text-Driven Techniques. Image credit: Gorodenkoff/Shutterstock
*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.
The focus shifted from conventional video editing methods to prioritize score distillation sampling, bypassing the standard reverse diffusion process and initiating optimization from videos already imbued with natural motion. Their analysis uncovered a dual effect: while video score distillation adeptly introduced new content as specified by the target text, it also induced notable deviations in structure and motion.
To mitigate this issue, the researchers proposed a solution: aligning space-time self-similarities between the original video and the edited version during the score distillation process. Leveraging score distillation rendered their approach model-agnostic and applicable across both cascaded and non-cascaded video diffusion frameworks.
Related Work
In previous work, advancements in diffusion models led to significant progress in text-driven image generation, benefiting from large-scale text-image pairs. However, extending these techniques to video editing posed challenges in accurately modeling real-world motion throughout the reverse diffusion process. Despite efforts such as inflating attention layers and integrating denoising processes with structural cues, zero-shot video editing remained challenging due to the need for more rich temporal priors in pre-trained models.
Recent approaches focused on self-supervised strategies and fine-tuning pre-trained model weights on input video motion. Nonetheless, the conventional reverse diffusion process struggled to reprogram complex motion without additional visual conditions or overfitting to specific spatial-temporal priors.
Optimization Techniques in DreamMotion
In DreamMotion, the primary objective is to generate an edited video that maintains the structural integrity and motion of the input video frames while faithfully reflecting a specified target text. The process begins with initializing a target video variable using the original video frames. Researchers conducted optimization using a three-pronged approach: Latent variable delta denoising score (LV-DDS) for appearance alignment, LS-SSM (self-similarity matching) for structural consistency, and LT-SSM for temporal smoothing. While the conventional method of score distillation with self-attention (SDS) and DDS formulations effectively injects appearance based on the target text, it often leads to structural inaccuracies and motion deviations. To address this, DreamMotion introduces structural correction through SSM and temporal smoothing, which leverages self-similarity.
Researchers facilitated appearance injection through SDS, which aligns the target image with the target text. DDS further enhances this alignment by incorporating additional text-image pairs. DreamMotion introduces video score distillation with masked gradients (V-DDS) as an extension of this mechanism to video editing, providing reliable gradient directions for injecting appearance into videos.
However, blurriness and over-saturation persist, mitigated by additional mask conditioning. Structural correction is addressed through LS-SSM, ensuring structural consistency between the target and original videos by minimizing discrepancies in their self-similarity maps.
DreamMotion introduces temporal LT-SSM for further refinement, aiming to model temporal correlations and minimize artifacts such as localized distortions and flickering. The integration of these optimization techniques into a cascaded video diffusion model framework is streamlined, primarily focusing on the initial keyframe generation stage. It allows for computational efficiency while ensuring that the refined keyframes undergo additional processing through the temporal interpolation and spatial super-resolution stages to generate the final edited video.
Experiment and Evaluation Results
Researchers conducted experiments to evaluate DreamMotion's performance in non-cascaded and cascaded video diffusion frameworks. Researchers utilized 26 text-video pairs from public datasets for the non-cascaded framework, conducting optimization using the ZeroScope model. Baseline methods include tune-a-video, controlvideo, control-a-video, gen-1, and tokenflow, each representing different approaches to video editing.
Qualitative results demonstrate that DreamMotion produces videos that adhere closely to the target prompt while preserving the motion of the input video, a feat not achieved by other baselines. Quantitatively, the approach outperforms baselines regarding textual alignment, frame consistency, and structure and motion preservation, as evidenced by automatic metrics and user study ratings.
DreamMotion is applied to 8-frame videos in the cascaded framework using the Show-1 model. Comparison against video manipulation consistency (VMC), a notable cascaded video editing approach, reveals DreamMotion's superior performance in preserving structure and motion integrity. Both qualitative and quantitative evaluations support the method's effectiveness in this framework.
Ablation studies highlight the importance of selective gradient filtering during LV-DDS updates and the necessity of self-similarity guidance for maintaining structural integrity and motion fidelity throughout the optimization process. Including spatial and temporal self-similarity alignments significantly improves the precision of video editing and enhances visual fidelity, demonstrating the effectiveness of the proposed techniques in addressing structural inaccuracies and motion deviations.
Conclusion
To sum up, DreamMotion presented a novel approach to diffusion-based video editing, overcoming challenges in maintaining real-world motion consistency. This framework effectively integrated target text-driven edits by leveraging score distillation optimization and space-time self-similarity alignment while preserving the original video's structural integrity and motion.
Extensive validation in both non-cascaded and cascaded settings demonstrated superior performance compared to traditional methods. However, limitations were significant, particularly in scenarios requiring substantial structural changes. Additionally, ethical considerations were paramount, given the potential misuse of generative models for creating misleading content.
*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.