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AI researchers from NVIDIA presented GET3D, a new generative model capable of creating fully-textured 3D meshes with complex topology and rich geometric details. Trained with a collection of 2D images, the AI is able to generate a huge variety of assets, including cars, chairs, animals, motorbikes, human characters, and buildings. According to the team, the feat was achieved thanks to recent success in the differentiable surface modeling, differentiable rendering, as well as 2D GANs.
"We generate a 3D SDF and a texture field via two latent codes. We utilize DMTet to extract a 3D surface mesh from the SDF and query the texture field at surface points to get colors. We train with adversarial losses defined on 2D images. In particular, we use a rasterization-based differentiable renderer to obtain RGB images and silhouettes. We utilize two 2D discriminators, each on RGB image, and silhouette, respectively, to classify whether the inputs are real or fake," comments the team.
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