We use the model architecture provided by
TripoSR, which is a Transformer model for 2D-to-3D mapping built on
LRM.
We scale it further on in-the-wild image collections by enabling unsupervised self-training and automatric data curation.
Training Data:
Real3D is jointly trained on synthetic data (Objaverse) and in-the-wild image collections. The former prevents training divergence, the latter introduces new knowldege from a broader distribution of real images. We use Objaverse renderings from
Zero-1-to-3 and
GObjaverse. The in the wild images are from
ImageNet,
OpenImages, etc.