MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video Generation (2502.04299v1)

Abstract: This paper presents a method that allows users to design cinematic video shots in the context of image-to-video generation. Shot design, a critical aspect of filmmaking, involves meticulously planning both camera movements and object motions in a scene. However, enabling intuitive shot design in modern image-to-video generation systems presents two main challenges: first, effectively capturing user intentions on the motion design, where both camera movements and scene-space object motions must be specified jointly; and second, representing motion information that can be effectively utilized by a video diffusion model to synthesize the image animations. To address these challenges, we introduce MotionCanvas, a method that integrates user-driven controls into image-to-video (I2V) generation models, allowing users to control both object and camera motions in a scene-aware manner. By connecting insights from classical computer graphics and contemporary video generation techniques, we demonstrate the ability to achieve 3D-aware motion control in I2V synthesis without requiring costly 3D-related training data. MotionCanvas enables users to intuitively depict scene-space motion intentions, and translates them into spatiotemporal motion-conditioning signals for video diffusion models. We demonstrate the effectiveness of our method on a wide range of real-world image content and shot-design scenarios, highlighting its potential to enhance the creative workflows in digital content creation and adapt to various image and video editing applications.

An Overview of MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video Generation

The paper "MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video Generation" presents an innovative approach to address significant challenges in the domain of image-to-video (I2V) generation, specifically in enabling user-driven cinematic shot design. The primary focus is on integrating both camera and object motion controls into video diffusion models (VDMs), bridging classical computer graphics and modern video synthesis techniques, all without reliance on labor-intensive 3D training data.

The MotionCanvas framework is built upon resolving two primary challenges in I2V generation: capturing user intentions effectively in motion design and transforming these intentions into actionable inputs for video diffusion models. The authors introduce a novel Motion Signal Translation module, which translates high-level scene-space motion designs into screen-space control signals, addressing challenges posed by the disparity between user-intended 3D scene space movements and the 2D screen space inputs preferred by typical video diffusion models.

Key Contributions

• Scene-Aware Motion Design: The paper highlights the importance of scene-aware planning, where both object and camera movements can be intuitively designed by users. This is achieved through the novel use of scene-anchored bounding boxes and point trajectories, which users can manipulate directly on the image canvas to specify global and local motions.
• Motion Signal Translation: The translation of scene-space designs into screen-space signals allows the derived input to effectively inform the video synthesis model. By leveraging depth maps and monocular depth estimation, the system eschews the need for exhaustive 3D labeling, thus broadening the scope of potential training datasets and improving the variety of possible outputs.
• Motion Conditioning in Video Diffusion Models: Following translation, the system implements several dedicated conditioning mechanisms. For instance, tracking trajectories are encoded with Discrete Cosine Transform (DCT) coefficients for efficiency and robustness. Bounding-box sequences are transformed into spatiotemporal embeddings, effectively aligning user-specified motion designs with the diffusion-based video generation process.
• Auto-regressive Long Video Generation: The paper also addresses the challenge of generating longer video sequences through the introduction of MotionCanvas$_\text{AR}$. This system considers temporal context through auto-regressive generation with overlapping short video clips, ensuring continuity and narrative fluidity in the production of extended video content.

Implications and Future Directions

The implications of MotionCanvas extend to several domains within digital content creation. Primarily, it facilitates a more intuitive and flexible approach for filmmakers, animators, and digital artists seeking to transform static images into dynamic video sequences with precise control over the motion. By offering a solution that does not rely on costly 3D-related training data, the methodology poses opportunities for expansion into new datasets and applications, which traditionally lacked structured motion information.

Experimentally, MotionCanvas demonstrates strong performance, achieving superior adherence to user-defined motion designs compared to contemporary I2V methods. Its application potential is significant, aligning with the growing demand for generative tools in creative industries.

Future research may further optimize the computational efficiency of the model, potentially exploring novel generative architectures that can match or exceed the current fidelity while reducing processing time. Additionally, refining motion representation to better accommodate complex scenes with significant depth variation within objects could enhance the realism of generated content even further, expanding the system's applicability to a broader range of scenario types, including macro and architectural visualization.

In conclusion, the MotionCanvas framework represents a substantial advancement in enabling fine-grained user control over video synthesis from static images, positioning itself as a potentially valuable tool in cinematic and creative AI applications.