DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory (2308.08089v1)

Abstract: Controllable video generation has gained significant attention in recent years. However, two main limitations persist: Firstly, most existing works focus on either text, image, or trajectory-based control, leading to an inability to achieve fine-grained control in videos. Secondly, trajectory control research is still in its early stages, with most experiments being conducted on simple datasets like Human3.6M. This constraint limits the models' capability to process open-domain images and effectively handle complex curved trajectories. In this paper, we propose DragNUWA, an open-domain diffusion-based video generation model. To tackle the issue of insufficient control granularity in existing works, we simultaneously introduce text, image, and trajectory information to provide fine-grained control over video content from semantic, spatial, and temporal perspectives. To resolve the problem of limited open-domain trajectory control in current research, We propose trajectory modeling with three aspects: a Trajectory Sampler (TS) to enable open-domain control of arbitrary trajectories, a Multiscale Fusion (MF) to control trajectories in different granularities, and an Adaptive Training (AT) strategy to generate consistent videos following trajectories. Our experiments validate the effectiveness of DragNUWA, demonstrating its superior performance in fine-grained control in video generation. The homepage link is \url{https://www.microsoft.com/en-us/research/project/dragnuwa/}

• The paper introduces DragNUWA, a novel approach integrating text, image, and trajectory controls for fine-grained video generation.
• It employs an open-domain diffusion model with innovative features like Trajectory Sampler, Multiscale Fusion, and Adaptive Training for enhanced control.
• Experimental results demonstrate superior management of complex motions and camera dynamics, surpassing previous video generation methods.

An Analysis of DragNUWA: Fine-grained Control in Video Generation

The paper "DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory" explores the challenge of offering precise control in video content generation. Previous approaches mainly focused on singular aspects like text, image, or trajectory-based controls. However, these approaches encountered limitations in terms of their control granularity for video generation, particularly in handling complex motions and sequences. In this paper, the authors propose DragNUWA, an innovative solution designed to facilitate more nuanced video generation by integrating text, image, and trajectory as control mechanisms.

Methodology

DragNUWA adopts an open-domain, diffusion-based video generation model, which aims to offer enhanced control over video semantics, spatial aspects, and temporal elements concurrently. This is achieved through the integration of both existing modalities (text and image) with trajectory-based control, which has generally been less explored. The trajectory control implemented in this research is notable due to the introduction of novel features, which include a Trajectory Sampler (TS), Multiscale Fusion (MF), and Adaptive Training (AT).

• Trajectory Sampler (TS): This component addresses the limitation of early-stage trajectory control by allowing arbitrary trajectory sampling directly from open-domain video optical flows. By doing so, the model extends its applicability beyond constrained datasets like Human3.6M, facilitating the handling of intricate and curved trajectories in various domains.
• Multiscale Fusion (MF): The MF technique integrates trajectory, text, and image information at different levels within the UNet architecture. This fusion allows more nuanced control of motion and semantic attributes in generated video sequences.
• Adaptive Training (AT): The AT strategy consists of two phases to ensure stable video generation. The first stage employs dense optical flow during training to guarantee dynamic consistency, while the second stage incorporates user-friendly, sparse trajectories to adapt the model for practical applications.

Experimental Results and Implications

The evaluations conducted validate DragNUWA's capability to achieve fine-grained control in video generation. The model exhibits the capability to control complex trajectories, camera movements, and multiple objects simultaneously. The results demonstrate that DragNUWA can surpass existing video generation models, particularly in terms of offering precise control over video dynamics, such as motion direction and magnitude, while maintaining consistency and coherency with user-input conditions.

From a practical standpoint, DragNUWA's approach signifies a meaningful advance in AI-driven video synthesis, opening opportunities for its application in various domains including animation, automated cinematography, and educational content creation. Theoretically, this research may inspire further studies on multi-modal controls in video generation and the exploration of new domains where fine-grained control is crucial.

Future Directions

The proposed methodology raises intriguing possibilities for future research. Firstly, the scope of trajectory controls could be expanded to encompass additional environmental interactions, such as those in augmented reality applications. Secondly, integrating more sophisticated semantic control mechanisms, such as user-intent recognition, could potentially enhance user-friendliness and application versatility. Lastly, extending DragNUWA’s framework for real-time applications could revolutionize interactions in digital video creation technologies.

In summary, DragNUWA offers a comprehensive framework for finely controlled video generation. By addressing previous limitations and introducing novel components for open-domain trajectory control, it paves the way for more comprehensive and user-friendly video generation capabilities in AI research and application.