Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models
Diffusion4D is an innovative project aimed at advancing the field of 4D content generation. Utilizing sophisticated video diffusion models, Diffusion4D is designed to deliver fast and consistent spatial-temporal 4D outputs. Here’s a detailed look into its core components and offerings.
Project Background and Links
The project, carrying the title "Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models," is officially represented on several platforms, providing a comprehensive overview of its capabilities and applications. For those interested, more detailed insights can be accessed via its Project Page, and further technical details can be found in the Arxiv paper. Additionally, demonstration videos are available on both YouTube and Bilibili.
Demonstration Capabilities
Image-to-4D
The project showcases the transformation from static images to dynamic 4D visuals, exemplifying its ability to extrapolate temporal dimensions from single images.
Text-to-4D
Diffusion4D isn't limited to images, as it also includes a capability for generating 4D content from textual descriptions, leveraging advanced AI interpretations to visualize narratives.
3D-to-4D
Extending its utility further, the project enables the conversion of static 3D models into lively 4D animations, bringing objects to life in a way that's both temporally and spatially coherent.
Latest Updates
The project continuously evolves, with recent updates including the release of rendered data from curated datasets such as objaverse-xl and objaverse-1.0. This has encompassed the introduction of dynamic and static 3D orbital videos as well as monocular videos. The metadata necessary for understanding these datasets has also been shared, aiding researchers in data preparation and analysis.
4D Dataset Preparation
One of the central features of Diffusion4D is its comprehensive dataset preparation process. It begins with a large-scale collection of dynamic 3D datasets from Objaverse-1.0 and Objaverse-XL. Selection criteria, involving stringent empirical rules, are applied to curate these datasets. The Diffusion4D team offers pre-rendered datasets to facilitate easy access and reduce the need for extensive GPU usage during data preparation.
How to Render 4D Datasets
The project provides practical instructions on rendering 4D images using Blender. By cloning their repository and following a series of steps, users can render these high-resolution images on their systems. This involves downloading Blender, necessary 4D objects, and executing rendering scripts that allow for custom settings such as frame numbers, resolution, and viewing angles.
Acknowledgements and Future Work
Diffusion4D is built upon a foundation of previous research and open-source projects, demonstrating a collaborative effort in the scientific community. The project’s acknowledgment section thanks these contributors, underscoring the importance of shared knowledge in advancing technology. While the project has already released some of its code and datasets, further developments and releases are anticipated.
For those using Diffusion4D in research, there's encouragement to cite the official paper, thus contributing to the recognition and academic discourse surrounding this innovative work.