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Infinigen: procedural generation of 3D worlds
Infinigen (Princeton, CVPR 2023) builds natural 3D scenes from rules alone, with no external assets. What the paper measured, what it didn’t, and the cost.
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LucidSim: generated images to train a robot
MIT CSAIL’s LucidSim (CoRL 2024) trained a quadruped robot in simulation only, using AI-generated images. See the results, real and simulated.
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Semantic segmentation with synthetic images (2017)
In 2017, 100 Blender images per class raised PASCAL VOC segmentation from 52.80% to 55.47% mIoU. What that proves and what it leaves open to question.
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Bias in synthetic data: what a 2024 report says
A 2024 report lists the risks and biases of synthetic data, with no experiments of its own. What it says, where it is weak, and how to audit data origin.
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GAN-generated vs real data: the MIT study
An MIT study (ICLR 2022) trained only on GAN images: below real data on ImageNet, above it on cars. What the study does and doesn’t tell you.
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Introduction to synthetic data generation
What synthetic data is, what the literature shows, when it pays off and where it falls short. A first-timer’s guide for computer vision teams.
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