  We propose SegGen, a highly-effective training data generation method for
image segmentation, which pushes the performance limits of state-of-the-art
segmentation models to a significant extent. SegGen designs and integrates two
data generation strategies: MaskSyn and ImgSyn. (i) MaskSyn synthesizes new
mask-image pairs via our proposed text-to-mask generation model and
mask-to-image generation model, greatly improving the diversity in segmentation
masks for model supervision; (ii) ImgSyn synthesizes new images based on
existing masks using the mask-to-image generation model, strongly improving
image diversity for model inputs. On the highly competitive ADE20K and COCO
benchmarks, our data generation method markedly improves the performance of
state-of-the-art segmentation models in semantic segmentation, panoptic
segmentation, and instance segmentation. Notably, in terms of the ADE20K mIoU,
Mask2Former R50 is largely boosted from 47.2 to 49.9 (+2.7); Mask2Former Swin-L
is also significantly increased from 56.1 to 57.4 (+1.3). These promising
results strongly suggest the effectiveness of our SegGen even when abundant
human-annotated training data is utilized. Moreover, training with our
synthetic data makes the segmentation models more robust towards unseen
domains. Project website: https://seggenerator.github.io
