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OpenAI takes AI art to next level with the newly launched Point-E

  /  Latest News   /  OpenAI takes AI art to next level with the newly launched Point-E

OpenAI takes AI art to next level with the newly launched Point-E

OpenAI open-sourced Point-E, an ML system that creates a 3D object given a text prompt

OpenAI creates a new standard in AI art with DALL-E 2. The multimodal model generates impressive, versatile, and creative motifs and can modify existing images to match the style. As a description one sentence is enough, several sentences work even better and create a more detailed picture.

The next breakthrough to take the AI world by storm might be 3D model generators.

Recently, OpenAI takes AI art to next level with the newly launched Point-E, a machine learning system that creates a 3D object given a text prompt.

Point-E is basically an AI that generates 3D models. According to a paper published alongside the code base, Point-E can produce AI art of 3D models in one to two minutes on a single Nvidia V100 GPU. Point-E doesn’t create 3D objects in the traditional sense.

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Rather, it generates point clouds, or discrete sets of data points in space that represents a 3D shape — hence the cheeky abbreviation. (The “E” in Point-E is short for “efficiency,” because it’s ostensibly faster than previous 3D object generation approaches.) Point clouds are easier to synthesize from a computational standpoint, but they don’t capture an object’s fine-grained shape or texture — a key limitation of Point-E presently. To get around this limitation, the Point-E team trained an additional AI system to convert Point-E’s point clouds to meshes. But they note in the paper that the model can sometimes miss certain parts of objects, resulting in blocky or distorted shapes.

Outside of the mesh-generating model, which stands alone, Point-E consists of two models: a text-to-image model and an image-to-3D model. The text-to-image model, similar to generative art systems like OpenAI’s own DALL-E 2 and Stable Diffusion, was trained on labeled images to understand the associations between words and visual concepts. The image-to-3D model, on the other hand, was fed a set of images paired with 3D objects so that it learned to effectively translate between the two.

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When given a text prompt — for example, “a 3D printable gear, a single gear 3 inches in diameter and half-inch thick” — Point-E’s text-to-image model generates a synthetic rendered object that’s fed to the image-to-3D model, which then generates a point cloud.

After training the models on a dataset of “several million” 3D objects and associated metadata, Point-E could produce colored point clouds that frequently matched text prompts, the OpenAI researchers say. It’s not perfect — Point-E’s image-to-3D model sometimes fails to understand the image from the text-to-image model, resulting in a shape that doesn’t match the text prompt.

Still, it’s orders of magnitude faster than the previous state-of-the-art — at least according to the OpenAI team.