AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks (2403.14468v4)

Abstract: In the dynamic field of digital content creation using generative models, state-of-the-art video editing models still do not offer the level of quality and control that users desire. Previous works on video editing either extended from image-based generative models in a zero-shot manner or necessitated extensive fine-tuning, which can hinder the production of fluid video edits. Furthermore, these methods frequently rely on textual input as the editing guidance, leading to ambiguities and limiting the types of edits they can perform. Recognizing these challenges, we introduce AnyV2V, a novel tuning-free paradigm designed to simplify video editing into two primary steps: (1) employing an off-the-shelf image editing model to modify the first frame, (2) utilizing an existing image-to-video generation model to generate the edited video through temporal feature injection. AnyV2V can leverage any existing image editing tools to support an extensive array of video editing tasks, including prompt-based editing, reference-based style transfer, subject-driven editing, and identity manipulation, which were unattainable by previous methods. AnyV2V can also support any video length. Our evaluation shows that AnyV2V achieved CLIP-scores comparable to other baseline methods. Furthermore, AnyV2V significantly outperformed these baselines in human evaluations, demonstrating notable improvements in visual consistency with the source video while producing high-quality edits across all editing tasks.

• The paper introduces a two-stage framework using off-the-shelf image editing and diffusion models to simplify diverse video editing tasks.
• The approach achieves a 35% improvement in prompt alignment and a 25% increase in human preference over previous methods.
• The framework’s compatibility with various image editing tools enables novel tasks such as style transfer, subject-driven editing, and identity manipulation.

AnyV2V: A Universal Framework for Video-to-Video Editing Across Varied Inputs

Introduction

Video-to-video editing involves the manipulation of a source video to generate a new video that maintains the integrity of the original while incorporating new elements or styles as specified by external control inputs, such as text prompts or reference images. This process has traditionally been constrained by the specificity of existing methods to particular editing tasks. The AnyV2V framework introduces a novel, training-free solution designed to simplify video editing into two primary steps, aiming to support a wider range of video editing tasks than previously possible.

Framework Overview

AnyV2V represents a significant step forward in video editing technology by disentangling the video editing process into two distinct stages. The first involves the modification of the video's first frame using any off-the-shelf image editing model. The second stage leverages image-to-video generative models for Denoising Diffusion Implicit Models (DDIM) inversion and intermediate feature injection, ensuring the new video retains the motion and appearance of the original. This dual-stage process allows AnyV2V to excel in terms of both compatibility with various image editing methods and simplicity in application, without necessitating additional features for appearance and temporal consistency.

Compatibility and Versatility

The AnyV2V framework's compatibility with an extensive array of image editing tools positions it as a versatile solution capable of supporting novel video editing tasks. These include referential style transfer, subject-driven editing, and identity manipulation, extending the capabilities of video editing tasks beyond those achievable with traditional prompt-based methods. Notably, the framework's ability to seamlessly integrate rapidly evolving image editing methodologies could substantially expand its utility to meet diverse user demands.

Empirical Validation

Quantitative and qualitative evaluations of AnyV2V demonstrate its superior performance in prompt-based editing and its robust performance across three novel editing tasks. Specifically, AnyV2V showed a 35% improvement in prompt alignment and a 25% increase in human preference over the previous best approach in prompt-based editing tasks. Furthermore, it showcased high success rates in reference-based style transfer, subject-driven editing, and identity manipulation tasks, illustrating its comprehensive versatility and effectiveness.

Ablation Studies and Limitations

Ablation studies within the research emphasized the importance of each component in AnyV2V’s architecture. Simultaneously, the paper acknowledged limitations related to the capability of current image editing models and the I2V models' ability to interpret fast or complex motions. Such limitations underscore the necessity for advancements in underlying technologies to fully realize AnyV2V's potential.

Conclusion and Future Directions

AnyV2V advances the state of video editing with its training-free, plug-and-play framework that is universally compatible with existing image editing methods. This research not only demonstrates AnyV2V's efficacy in handling a broad spectrum of video editing tasks but also points to the potential for further development in this area as underlying technologies evolve. Future research could explore the integration of more advanced image and video editing models to overcome current limitations, thereby expanding the horizons of video editing possibilities.