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MotionCtrl: A Unified and Flexible Motion Controller for Video Generation (2312.03641v2)

Published 6 Dec 2023 in cs.CV, cs.AI, cs.LG, and cs.MM

Abstract: Motions in a video primarily consist of camera motion, induced by camera movement, and object motion, resulting from object movement. Accurate control of both camera and object motion is essential for video generation. However, existing works either mainly focus on one type of motion or do not clearly distinguish between the two, limiting their control capabilities and diversity. Therefore, this paper presents MotionCtrl, a unified and flexible motion controller for video generation designed to effectively and independently control camera and object motion. The architecture and training strategy of MotionCtrl are carefully devised, taking into account the inherent properties of camera motion, object motion, and imperfect training data. Compared to previous methods, MotionCtrl offers three main advantages: 1) It effectively and independently controls camera motion and object motion, enabling more fine-grained motion control and facilitating flexible and diverse combinations of both types of motion. 2) Its motion conditions are determined by camera poses and trajectories, which are appearance-free and minimally impact the appearance or shape of objects in generated videos. 3) It is a relatively generalizable model that can adapt to a wide array of camera poses and trajectories once trained. Extensive qualitative and quantitative experiments have been conducted to demonstrate the superiority of MotionCtrl over existing methods. Project Page: https://wzhouxiff.github.io/projects/MotionCtrl/

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Citations (106)
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Summary

  • The paper presents MotionCtrl, a unified motion controller that integrates both camera and object motion modules for enhanced video generation.
  • It leverages diffusion models with dedicated CMCM and OMCM modules to achieve superior motion control, as evidenced by lower Euclidean distances in evaluations.
  • MotionCtrl employs innovative training strategies and dataset augmentation to provide precise, adaptable, and high-fidelity text-to-video generation.

Insightful Overview of "MotionCtrl: A Unified and Flexible Motion Controller for Video Generation"

The paper presents MotionCtrl, a motion control mechanism explicitly designed for video generation, targeting the precise manipulation of both camera and object motions within generated videos. Leveraging the capabilities of diffusion models, particularly in the context of text-to-video (T2V) frameworks, MotionCtrl emerges as a novel architecture offering a unified approach to handle complex video dynamics with enhanced flexibility and independence between motion types.

MotionCtrl is grounded in two primary modules: the Camera Motion Control Module (CMCM) and the Object Motion Control Module (OMCM). The CMCM targets global scene transformations dictated by camera pose sequences, effectively extending temporal transformers within the Latent Video Diffusion Model (LVDM). Meanwhile, the OMCM leverages object-specific trajectories, spatially integrated through convolutional layers within the model, to govern movement patterns at the pixel cluster level associated with dynamic objects. This dual-module setup enables MotionCtrl to manage motion intricacies with remarkable granularity and precision, surpassing the bifurcated or singular approaches of previous methodologies.

Performance evaluation highlights MotionCtrl's prominence in motion control compared to other state-of-the-art systems such as AnimateDiff and VideoComposer. Specifically, MotionCtrl exhibits superior performance in terms of both execution accuracy and adaptability, validated through lower Euclidean distances in motion capture evaluations (CamMC and ObjMC metrics). The model's ability to independently and flexibly manipulate generated video content through specified camera and object dynamics significantly enhances the fidelity and applicability of generated sequences.

Remarkably, the model introduces innovative training strategies to address the challenge of missing comprehensive training data featuring joint annotations of captions, camera poses, and object trajectories. By creatively augmenting existing datasets and applying strategic fine-tuning only where necessary, MotionCtrl ensures robust adaptability and performance while disentangling the complexities of diverse motion control in video generation.

In practical applications, MotionCtrl's architecture and methodological innovations imply substantial potential for multimedia content creation, particularly in automating video sequences that adhere to specified textual prompts. Theoretically, the paper pushes the boundaries of how diffusion models and motion-enhanced frameworks can function symbiotically within AI systems, pointing toward future research opportunities in refining motion models for seamless integration into diverse real-world scenarios.

This research illustrates the capacity to harness motion control in video generation without compromising on the quality and coherence of outputs, setting a new benchmark in video synthesis methodologies. As MotionCtrl bridges the gap between theoretical development and practical deployment, it sets a foundation for more sophisticated video generation techniques capable of supporting varied applications across entertainment, media, and virtual realities.

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