


Efficient 3D model representation: Images and videos can directly obtain latent features through 2D/2.5D matrix representation compression. In contrast, 3D data has complex topology and higher representation dimensions. How to efficiently compress 3D data and then analyze and learn the distribution of 3D data in 3D latent space is a problem that has always troubled industry personnel.
Efficient 3D training architecture: DiT architecture was first applied in the field of image generation and achieved great success, including Stable Diffusion 3 (SD3), Hunyuan-DiT All adopt the DiT architecture; in the field of video generation, OpenAI SORA uses the DiT architecture to successfully achieve video generation effects that far exceed Runway and Pika; in the field of 3D generation, limited by complex topology and three-dimensional representation methods, the original DiT architecture cannot directly Applied to 3D mesh generation.
High-quality large-scale 3D training data: The quality and scale of 3D training data directly determine the quality and generalization ability of the generated model. It is generally believed in the industry that at least Tens of millions of high-quality 3D training data are needed to meet the training requirements of large 3D models. However, 3D data is extremely scarce around the world. Although there are tens of millions of 3D training data sets such as ObjaverseXL, most of them are low-quality simple structures, and the available high-quality 3D data accounts for less than 5 %. How to obtain a sufficient amount of high-quality 3D data is a worldwide problem.
In response to the above core problems, DreamTech proposed the world's first native 3D-DiT large model Direct3D. Through extensive experimental verification, the 3D model generation quality of Direct3D significantly surpasses the current mainstream 2D dimensionality method, which mainly benefits from the following three points:
D3D-VAE : Direct3D proposes a 3D VAE (Variational Auto-Encoder) similar to OpenAI SORA to extract latent features of 3D data, reducing the representation complexity of 3D data from the original N^3 to n^2 (n
D3D-DiT: Direct3D adopts the DiT architecture and improves and optimizes the original DiT. It introduces semantic-level and pixel-level alignment modules for input images to achieve output The model is aligned to the height of any input image.
DreamTech 3D Data Engine: Direct3D uses a large amount of high-quality 3D data in training, most of which is produced by DreamTech's self-developed data synthesis engine become. The DreamTech synthesis engine has established fully automatic data processing processes such as data cleaning and annotation, and has accumulated and produced more than 20 million high-quality 3D data, completing the last piece of the puzzle for the implementation of native 3D algorithms. It is worth mentioning that OpenAI tried to use millions of 3D synthetic data in the training process of Shap-E and Point-E in 2023. Compared with OpenAI’s data synthesis solution, the 3D data synthesized by DreamTech is larger in scale, and Higher quality.
The model geometry is distorted and prone to long heads and tails;
The model has many sharp burrs;
The surface is overly smooth and lacks details;
The mesh has a small number of patches. Fine structure cannot be guaranteed.


The above is the detailed content of The 3D version of SORA is here! DreamTech launches Direct3D, the world's first native 3D-DiT large model. For more information, please follow other related articles on the PHP Chinese website!

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