Researchers from Princeton Introduce Infinigen: A Procedural Generator of Photorealistic 3D Scenes of the Natural World

The research team from Princeton University has introduced Infinigen, a groundbreaking procedural generator for photorealistic 3D scenes, in their recent paper titled “Infinite Photorealistic Worlds using Procedural Generation.” This work addresses the limitations of existing synthetic datasets that offer limited diversity and fail to capture the complexity of real-world objects.

Infinigen is a fully procedural system that enables the generation of an infinite number of shapes, textures, materials, and scene compositions from scratch. Its key feature lies in its ability to produce high levels of photorealism by procedurally generating both coarse and fine geometric and textural details. Infinigen is separated because all the geometric information it generates is based on real-world references, enhancing the authenticity of the synthetic scenes.

The architecture of Infinigen is built upon Blender, a widely used graphics system known for its capabilities in procedural generation. The research team has designed and implemented a library of procedural rules to expand the coverage of natural objects and scenes. These rules leverage the useful primitives available in Blender. Moreover, the team has developed utilities that simplify the creation of procedural rules, including an automatic conversion tool that transforms Blender node graphs into Python code. Additionally, utilities have been developed to render synthetic images with ground truth labels, providing information such as depth, occlusion boundaries, bounding boxes, optical flow, surface normals, object categories, and instance segmentation.

To evaluate the quality of the synthetic data generated by Infinigen, the team conducted extensive experiments and compared it with existing synthetic datasets and generators. The results of these experiments demonstrate Infinigen’s remarkable capability to produce photorealistic and original assets and scenes without relying on external sources. This showcases its potential for generating a diverse and expansive training dataset that more accurately reflects the complexity of the real world.

Infinigen is an open-source project that the researchers intend to nurture as a collaborative effort with the wider community. They are committed to expanding its coverage to encompass all real-world elements, ensuring its continued development and growth. By offering Infinigen as a freely available resource, the research team hopes to foster collaboration and inspire further advancements in procedural generation.

Overall, the introduction of Infinigen marks a significant advancement in generating synthetic data for computer vision tasks. Its procedural approach, coupled with its ability to produce photorealistic scenes, promises to bridge the gap between existing synthetic datasets and the complexity of real-world objects, making it an invaluable tool for training models in various computer vision applications.

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