Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control (2501.03847v2)

Abstract: Diffusion models have demonstrated impressive performance in generating high-quality videos from text prompts or images. However, precise control over the video generation process, such as camera manipulation or content editing, remains a significant challenge. Existing methods for controlled video generation are typically limited to a single control type, lacking the flexibility to handle diverse control demands. In this paper, we introduce Diffusion as Shader (DaS), a novel approach that supports multiple video control tasks within a unified architecture. Our key insight is that achieving versatile video control necessitates leveraging 3D control signals, as videos are fundamentally 2D renderings of dynamic 3D content. Unlike prior methods limited to 2D control signals, DaS leverages 3D tracking videos as control inputs, making the video diffusion process inherently 3D-aware. This innovation allows DaS to achieve a wide range of video controls by simply manipulating the 3D tracking videos. A further advantage of using 3D tracking videos is their ability to effectively link frames, significantly enhancing the temporal consistency of the generated videos. With just 3 days of fine-tuning on 8 H800 GPUs using less than 10k videos, DaS demonstrates strong control capabilities across diverse tasks, including mesh-to-video generation, camera control, motion transfer, and object manipulation.

An Overview of "Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control"

The paper "Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control" presents an innovative approach to controlled video generation. The authors introduce Diffusion as Shader (DaS), a model that leverages 3D-aware diffusion techniques to enable diverse video control tasks, expanding the capabilities of diffusion-based generative models.

Core Contributions and Methodology

The primary contribution of this research lies in the introduction of 3D tracking videos as a control signal for video generation. The authors argue that traditional methods relying on 2D control signals are limited in providing fine-grained and versatile control over video output. By employing 3D tracking videos, DaS achieves improved temporal consistency and enables precise control over various aspects of video generation.

The paper details the implementation of DaS, which diffuses video sequences conditioned on 3D tracking videos that encode the motion trajectories of 3D points. These points maintain consistent color attributes representing their initial spatial positions, which contribute to the coherent appearance of dynamic content across frames. This 3D-aware diffusion framework allows for several types of controlled video synthesis, including mesh-to-video generation, motion transfer, camera control, and object manipulation.

Results and Evaluation

The model is evaluated across several tasks, demonstrating its flexibility and robustness. Key evaluations include:

• Camera Control: DaS outperforms existing baseline methods such as MotionCtrl and CameraCtrl in both translation and rotation error metrics. The use of 3D tracking information allows DaS to accurately generate videos with specified camera trajectories and framespace consistency.
• Motion Transfer: The authors evaluate the alignment of the generated video content with text prompts and the temporal consistency among consecutive frames using CLIP scores. DaS exhibits superior performance in both regards, demonstrating its ability to maintain semantic alignment and coherency.
• Animating Meshes to Videos: The approach is compared with state-of-the-art methods like CHAMP, showing better preservation of 3D structures and texture details across different motion sequences or styles.
• Object Manipulation: By manipulating a subset of 3D points, DaS is shown to effectively implement complex object movements, demonstrating flexibility in generating photorealistic, consistent video outputs.

The model's capability is backed by numerical results that illustrate its performance across various video control tasks. The new method efficiently utilizes limited datasets for fine-tuning, showing strong control capabilities with minimal computational resources.

Theoretical and Practical Implications

The introduction of 3D control signals via tracking videos opens up new avenues for developing sophisticated video generation models capable of nuanced control. Theoretically, this shifts the paradigm from 2D-based diffusion control approaches to more semantically meaningful generative mechanisms that integrate spatial-temporal coherence effectively.

Practically, this research has potential applications across diverse fields including animation, virtual reality, video editing, and even interactive game development, where high fidelity and user-customizable video content are crucial. The ability to accurately generate complex video trajectories and motions using limited resources makes DaS a compelling tool for many commercial and creative industries.

Future Directions

Future work could explore enhancing the automatic generation of 3D tracking videos to increase its applicability across a broader range of use cases. Additionally, addressing the identified limitations, such as correctly matching input images to tracking videos for optimal scene transition, could significantly improve the reliability of output videos. The integration of more advanced depth map estimation techniques may also provide further improvements in the quality of the generated videos.

In conclusion, the development and implementation of DaS mark a significant step forward in controlled video generation, enabling a level of versatility and precision previously unattained in generative video models. The work highlights the immense potential of incorporating 3D spatial awareness into diffusion processes, paving the way for future research and application enhancements.