Anyverse
Hyperspectral synthetic data platform for high-fidelity perception model training and validation.

Infinigen, developed by the Princeton Vision & Learning Lab, represents a paradigm shift in 3D scene synthesis. Unlike generative AI models that produce pixels or voxels based on statistical patterns, Infinigen utilizes a purely procedural approach to construct high-fidelity 3D worlds from mathematical primitives. Built on the Blender ecosystem and leveraging the Cycles engine, it generates complex geometry, realistic materials, and dynamic lighting for natural environments including terrains, flora, and underwater scenes. By 2026, it has solidified its position as a critical tool for Computer Vision researchers and Robotics engineers, providing an infinite source of diverse training data with pixel-perfect ground truth annotations (depth, segmentation, surface normals). The system is designed to be extensible, allowing developers to inject custom procedural rules to generate niche environments. Its architecture is specifically optimized for high-performance compute clusters, enabling the parallel generation of thousands of unique, physically plausible scenes that are indistinguishable from real-world photography in the eyes of neural network training pipelines.
Uses randomized math-based geometry nodes instead of mesh databases to create assets.
Hyperspectral synthetic data platform for high-fidelity perception model training and validation.
Hyper-realistic synthetic data generation for high-fidelity computer vision training.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Outputs per-pixel semantic segmentation, depth, surface normals, and optical flow natively.
Integration with Blender's Cycles engine for path-traced global illumination.
Procedurally determines the level of detail based on camera distance and frustum.
Exports to .blend and can be converted to USD or GLTF formats.
Parameters for rain, fog, and snow integrated into the shader and geometry stack.
Specific modules for generating biologically accurate plants and creatures.
Lack of diverse forest environments for obstacle avoidance testing.
Registry Updated:2/7/2026
Scarcity of labeled high-resolution satellite data for varying biomes.
Needs thousands of random organic shapes to test tactile sensors.