3D-Consistent Video Generation with Explicit 3D Modeling through Diffusion

The rapid advancements in the field of diffusion models have set new standards in image and video generation, enabling realistic visual synthesis in single and multi-image contexts. However, the efficient and explicit generation of 3D-consistent content remains a challenge. A promising approach to overcome this hurdle is the integration of explicit 3D information into the generation process.

The Challenge of 3D Consistency

Conventional diffusion models used for video and multi-view synthesis mostly learn consistency between images implicitly, for example, through attention mechanisms in neural networks. This implicit learning method, however, can lead to inconsistencies in the 3D representation, especially in complex scenes and camera movements. The explicit modeling of 3D information offers an alternative by directly incorporating geometric relationships into the generation process.

XYZ Images as the Key to 3D Integration

A promising approach to integrating 3D information is the use of so-called XYZ images. These images encode the global 3D coordinates for each pixel, thus providing a direct representation of the geometry of a scene. In contrast to RGB images, which contain complex texture and lighting information, XYZ images focus exclusively on the geometric structure. This property makes them particularly suitable for integration into diffusion models, as they provide clear and unambiguous 3D information.

World-consistent Video Diffusion (WVD)

An example of the application of XYZ images in diffusion models is the World-consistent Video Diffusion (WVD) framework. WVD trains a diffusion transformer to learn the joint distribution of RGB and XYZ images. By combining RGB and XYZ information, the model can generate both realistic textures and consistent 3D structures. WVD's flexible inpainting strategy also allows the model to be applied to various tasks, such as estimating XYZ images from RGB images or generating new RGB images along a given camera trajectory.

Versatile Application Possibilities

By integrating explicit 3D information into the generation process, a variety of application possibilities open up. WVD can be used, for example, for the generation of 3D models from single images, multi-view stereo reconstruction, or camera-controlled video generation. The ability to handle different tasks with a single pre-trained model underscores the potential of WVD as a basis for future 3D-consistent generation models.

Future Developments

The integration of explicit 3D modeling into diffusion models is a promising research area with great potential. Future work could focus on improving scalability and extending to more complex scenes and datasets. The development of more efficient training methods and the exploration of new 3D representations could also contribute to further progress in this area. Combining diffusion models with other 3D techniques, such as volume rendering, could also lead to interesting new approaches.

Conclusion

Explicit 3D modeling in diffusion models, as demonstrated in the WVD framework, offers a promising path towards the generation of 3D-consistent content. The use of XYZ images enables effective integration of geometric information into the generation process and opens up new possibilities for image and video creation. Further research into this approach could lead to significant advancements in the field of 3D generation and enable new applications in various areas.