I2VControl: Disentangled and Unified Video Motion Synthesis Control (2411.17765v2)

Abstract: Video synthesis techniques are undergoing rapid progress, with controllability being a significant aspect of practical usability for end-users. Although text condition is an effective way to guide video synthesis, capturing the correct joint distribution between text descriptions and video motion remains a substantial challenge. In this paper, we present a disentangled and unified framework, namely I2VControl, that unifies multiple motion control tasks in image-to-video synthesis. Our approach partitions the video into individual motion units and represents each unit with disentangled control signals, which allows for various control types to be flexibly combined within our single system. Furthermore, our methodology seamlessly integrates as a plug-in for pre-trained models and remains agnostic to specific model architectures. We conduct extensive experiments, achieving excellent performance on various control tasks, and our method further facilitates user-driven creative combinations, enhancing innovation and creativity. The project page is: https://wanquanf.github.io/I2VControl .

• The paper introduces a unified video synthesis control framework that disentangles motion units for precise manipulation.
• It partitions video content into brush-units, drag-units, and a borderland, enabling flexible, conflict-free motion control.
• Empirical results demonstrate improved control flexibility and video quality compared to recent models, enhancing creative applications.

Overview of I2VControl: Disentangled and Unified Video Motion Synthesis Control

The paper presents I2VControl, an innovative framework developed for image-to-video motion synthesis control, aiming to enhance the controllability of video synthesis—a key factor for practical user applications. This research introduces a unified approach that integrates multiple motion control tasks within a single framework, providing a versatile and cohesive solution to the challenges associated with current video synthesis capabilities.

Key Contributions and Methodology

I2VControl addresses the limitations of prior methods that often focus on single types of motion patterns or require data formats customized for specific applications. Different from these methods, I2VControl partitions video content into individual motion units, namely brush-units, drag-units, and a borderland, each characterized by disentangled control signals. This structural decomposition allows for flexible, conflict-free combinations of varied control types within a unified system.

The brush-units are defined by a motion strength value that affects specific parts of a video through user-input scalar adjustments. Drag-units, on the other hand, enable complex control with six degrees of freedom, allowing precise manipulation of on-screen elements. The borderland serves as a nearly static background, seamlessly integrating with other dynamic elements.

The proposed methodology is implemented as a plug-in, compatible with pre-trained models, ensuring its architecture remains agnostic across various model structures. This flexibility allows I2VControl to act as an additional layer of functionality, providing enhanced control for video synthesis models without the need for architectural restructuring.

Empirical Evaluation and Results

I2VControl was subjected to extensive experiments across different scenarios, including camera movement, object movement, and motion brushing, demonstrating excellent performance. The numerical results indicate significant improvement in control flexibility and effectiveness when applied to diverse synthesis tasks. Furthermore, the integration of user-driven creative combinations was observed to stimulate imaginative video productions, thereby enhancing innovation and artistic output.

The adaptability of the proposed framework is highlighted through superior metrics in motion trajectory and control precision. Compared to recent models such as DragAnything and MOFA-Video, I2VControl showcases better compliance with user-defined motion signals and enhanced video quality metrics like FID.

Implications and Future Work

The introduction of I2VControl presents numerous implications for both practical applications and theoretical advancements in AI-driven video synthesis. Practically, it offers end-users—especially those in creative industries like film production, gaming, and social media—the ability to generate video content with unprecedented levels of control and precision. Theoretically, it paves the way for future research into more sophisticated integration of multimodal input signals, potentially expanding into audio and text-driven video manipulation.

Looking forward, further exploration could include enhancing network architectures to support more nuanced controls and extending control capabilities beyond current trajectory and motion-based inputs. Such advancements would further solidify the framework's role as a leading tool in the domain of image-to-video synthesis, expanding its utility and application scope.

In conclusion, the paper significantly contributes to the field of video synthesis by providing a comprehensive, user-friendly framework that empowers users with comprehensive control capabilities, thus marking a step towards more interactive and customizable video content creation tools.