{"id":24856,"date":"2022-12-20T20:54:39","date_gmt":"2022-12-20T20:54:39","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2022\/12\/20\/openai-develops-ai-system-capable-of-generating-3d-models\/"},"modified":"2022-12-20T20:54:39","modified_gmt":"2022-12-20T20:54:39","slug":"openai-develops-ai-system-capable-of-generating-3d-models","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2022\/12\/20\/openai-develops-ai-system-capable-of-generating-3d-models\/","title":{"rendered":"OpenAI develops AI system capable of generating 3D models"},"content":{"rendered":"

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OpenAI LLC this week <\/span>detailed<\/span><\/a> POINT-E, a new artificial intelligence system that can generate 3D models based on text prompts.<\/span><\/p>\n

The research group has made the code for POINT-E available on <\/span>GitHub<\/span><\/a>.\u00a0<\/span><\/p>\n

There are multiple AI applications that can generate two-dimensional images based on a text description provided by a user. According to OpenAI, such applications render images in a few seconds or minutes when running on a single data center graphics card. In contrast, generating a 3D model typically takes a few hours when using comparable hardware.\u00a0<\/span><\/p>\n

OpenAI built POINT-E to speed up the process. According to the research group, POINT-E can generate a 3D model in as little as one minute when running on an Nvidia V100 graphics card.<\/span><\/p>\n

When the AI system receives a user prompt describing an object, it doesn\u2019t generate a 3D model of the object directly. Instead, the AI system first creates a two-dimensional drawing of the specified object. From there, POINT-E turns the two-dimensional drawing into a three-dimensional point cloud, which is a basic version of a 3D model that functions as an outline.\u00a0<\/span><\/p>\n

Each step of the process is carried out by a different neural network.\u00a0<\/span>The first step, which involves turning the user\u2019s description of an object into a two-dimensional drawing, is carried out by a neural network dubbed GLIDE that OpenAI originally released last year. The version of GLIDE used in POINT-E features three billion parameters. Parameters are the configuration settings that define how a neural network goes about processing data.<\/span><\/p>\n

After POINT-E generates a two-dimensional drawing of an object, the drawing is turned into a point cloud by two separate neural networks. The first neural network generates an initial, low-resolution point cloud with 1,000 pixels. The second algorithm, which is described as a simpler version of the first, adds 3,000 more pixels to increase the point cloud\u2019s resolution.<\/span><\/p>\n

\u201cFor image diffusion models, the best quality is typically achieved by using some form of hierarchy, where a low resolution base model produces output which is then upsampled by another model,\u201d OpenAI scientists detailed in a <\/span>research paper<\/span><\/a> explaining POINT-E. \u201cOur upsampler uses the same architecture as our base model.\u201d<\/span><\/p>\n

The neural networks that Point-E uses to generate 3D models are based on a machine learning method known as diffusion. The method, which was was first introduced in 2015, also powers an image generation AI that Google LLC debuted<\/a> earlier this year.\u00a0<\/span><\/p>\n

To build a diffusion model, engineers create images that contain a type of error known as Gaussian noise. They then task the diffusion model with removing this noise. By repeating the process many times, a neural network can learn techniques that allow it to generate new images from scratch.<\/span><\/p>\n

After POINT-E creates a point cloud of an object, it turns the point cloud into a 3D model with the help of Blender, an open-source graphic design application. The process of creating a 3D model in Blender is managed by an automated script.\u00a0<\/span><\/p>\n

\u201cWhile our method performs worse on this evaluation than state-of-the-art techniques, it produces samples in a small fraction of the time,\u201d OpenAI\u2019s researchers detailed. \u201cThis could make it more practical for certain applications, or could allow for the discovery of higher-quality 3D objects by sampling many objects and selecting the best one.\u201d<\/span><\/p>\n

Image: OpenAI<\/h5>\n
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