3D-Aware Video Generation: Diffusion as Shader Enables Versatile Control

The generation of videos using artificial intelligence (AI) has made remarkable progress in recent years. Diffusion models play a central role, delivering impressive results in creating videos from text descriptions or images. However, precise control of the generation process, such as manipulating camera movement or targeted editing of content, remains a challenge. Existing approaches to controlled video generation are mostly limited to a single control type and lack the flexibility to meet diverse requirements.

A new approach called "Diffusion as Shader" (DaS) promises a remedy. DaS supports various video control mechanisms within a unified architecture. The core of DaS lies in the use of 3D control signals, as videos are fundamentally 2D representations of dynamic 3D content. In contrast to previous methods, which were limited to 2D signals, DaS uses 3D tracking videos as control inputs. This makes the video generation process 3D-aware.

This innovation enables DaS to offer a wide range of video controls through simple manipulation of the 3D tracking videos. Another advantage of using 3D tracking videos lies in their ability to effectively link frames, which significantly improves the temporal consistency of the generated videos. After only three days of fine-tuning on eight H800 GPUs with less than 10,000 videos, DaS demonstrates strong control capabilities in various tasks.

Diverse Applications of DaS

DaS opens up new possibilities for controlled video generation and offers a range of application scenarios:

Mesh-to-Video Generation: Starting from 3D mesh models, realistic videos can be created. Control is achieved by animating the mesh in the 3D tracking video.

Camera Control: The camera movement in the generated video can be precisely controlled by adjusting the camera position and orientation in the 3D tracking video. This allows for dynamic camera movements and changes in perspective.

Motion Transfer: Movements from one video can be transferred to another. DaS extracts the motion from the 3D tracking video of a source video and applies it to the target video.

Object Manipulation: Objects in the video can be added, removed, or manipulated by making the corresponding changes in the 3D tracking video.

Technical Background and Functionality

The architecture of DaS is based on diffusion models, which generate images or videos through the iterative process of adding and removing noise. The 3D tracking videos serve as additional inputs for the diffusion model and influence the generation process. By manipulating the 3D tracking videos, various aspects of the generated video can be controlled. Fine-tuning the model on a relatively small dataset allows for efficient adaptation to specific tasks and domains.

Future Perspectives and Significance for Mindverse

DaS represents a promising approach for controlled video generation and has the potential to revolutionize the creation of high-quality videos. The technology opens up new possibilities for creative applications in various fields, including film, advertising, and video games. For Mindverse, as a provider of AI-powered content tools, DaS offers the opportunity to expand the platform's functionality and provide users with new creative tools. The integration of DaS into the Mindverse platform could enable the creation of personalized and interactive video content and significantly simplify the workflow for video production.

Bibliography
https://huggingface.co/papers/2501.03847
https://www.chatpaper.com/chatpaper/zh-CN/paper/96589
https://chatpaper.com/chatpaper/ja/paper/96589
https://arxiv.org/abs/2403.06738
https://github.com/ChenHsing/Awesome-Video-Diffusion-Models
https://www.cvlibs.net/publications/Schwarz2024ICLR.pdf
https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/08166.pdf
https://github.com/wangkai930418/awesome-diffusion-categorized
https://bohrium.dp.tech/paper/arxiv/2406.01476
https://dl.acm.org/doi/10.1145/3696415