ZhiYuan Launches See3D: Open-Source 3D Generation from Unlabeled Videos
date
Dec 10, 2024
damn
language
en
status
Published
type
News
image
https://www.ai-damn.com/1733849987415-6386944048455373767015634.png
slug
zhiyuan-launches-see3d-open-source-3d-generation-from-unlabeled-videos-1733851009835
tags
See3D
3D Generation Model
Video Learning
Artificial Intelligence
Open Source
summary
The Beijing Academy of Artificial Intelligence has unveiled See3D, an open-source 3D generation model that learns from unlabeled internet videos. The model offers scalable, cost-effective 3D generation without the need for expensive 3D annotations, setting a new benchmark for 3D research and applications.
ZhiYuan Introduces See3D: A Groundbreaking 3D Generation Model
The Beijing Academy of Artificial Intelligence (BAAI) has launched a pioneering open-source 3D generation model called See3D. This model is designed to learn from large-scale unlabeled internet videos, offering an efficient and scalable approach to 3D generation. The See3D model marks a significant advancement in the field by enabling 3D generation without relying on traditional 3D camera data or annotations. Instead, it leverages visual conditioning techniques to create geometrically consistent, camera-direction-controllable multi-view images solely from visual cues in the video.
Key Features of See3D
See3D allows for the generation of 3D models from text, single views, and sparse views. The model supports 3D editing and Gaussian rendering, making it versatile for various applications, including 3D interactive worlds and 3D reconstruction. Additionally, the model, code, and demo have been open-sourced to encourage further research and development in the 3D generation domain.
Practical Applications
Demonstrations of See3D's capabilities showcase its potential in multiple areas of 3D creativity. The model can be used for:
- Unlocking 3D interactive environments
- 3D reconstruction from sparse images
- Open-world 3D generation
- 3D generation from single views
These demonstrations highlight the model's ability to perform complex 3D tasks with ease, making it a powerful tool for industries such as virtual reality, gaming, and digital media creation.
Motivation Behind See3D
Traditional 3D data collection methods are often time-consuming and expensive. The high costs and limitations of acquiring labeled 3D data have long hindered progress in 3D modeling. With videos readily available online, which contain multi-view correlations and camera motion information, See3D capitalizes on these resources to learn 3D structures more efficiently.
The See3D team constructed the WebVi3D dataset, a large-scale collection of video data that includes 16 million clips and 320 million frames. This dataset, combined with advanced filtering techniques, forms the foundation for training the See3D model, allowing it to generate 3D structures based purely on visual signals. By adding time-dependent noise to the masked video data, the model supports scalable multi-view diffusion model training, generating 3D outputs without requiring camera-specific annotations.
Advantages of See3D
The See3D model offers several advantages over traditional methods:
- Data Scalability: See3D's use of large-scale internet videos as training data enables it to handle vast amounts of unstructured data, vastly increasing the scale of constructed multi-view datasets.
- Camera Controllability: The model can generate scenes with any camera trajectory, maintaining consistent geometry across frames, providing greater flexibility in 3D scene creation.
- Geometric Consistency: Despite using unlabeled video data, See3D ensures that the generated 3D outputs maintain geometric accuracy, which is essential for applications that require realism and precision.
By expanding the scale of its dataset, See3D sets new precedents for the development of 3D generation technology. The hope is that this advancement will shift the focus of the research community toward utilizing large-scale unlabeled video data and reducing the reliance on expensive, closed-source 3D solutions.
Future Implications
The launch of See3D opens new avenues for research in 3D modeling. It is expected that this breakthrough will lead to more accessible, cost-effective solutions for creating realistic 3D models. The open-source nature of See3D also invites contributions from the global research community, fostering innovation and collaboration in the field of 3D generation.
For more information, visit the official See3D project page.
Key Points
- See3D is an open-source 3D generation model that learns from unlabeled internet videos.
- It supports 3D generation from text, single views, and sparse views, as well as 3D editing and Gaussian rendering.
- See3D reduces the need for expensive 3D annotations by utilizing large-scale video data.
- The model ensures scalability, camera controllability, and geometric consistency.
- See3D is expected to make 3D generation more accessible and cost-effective for various industries.