  Recent advancements in generative models have unlocked the capabilities to
render photo-realistic data in a controllable fashion. Trained on the real
data, these generative models are capable of producing realistic samples with
minimal to no domain gap, as compared to the traditional graphics rendering.
However, using the data generated using such models for training downstream
tasks remains under-explored, mainly due to the lack of 3D consistent
annotations. Moreover, controllable generative models are learned from massive
data and their latent space is often too vast to obtain meaningful sample
distributions for downstream task with limited generation. To overcome these
challenges, we extract 3D consistent annotations from an existing controllable
generative model, making the data useful for downstream tasks. Our experiments
show competitive performance against state-of-the-art models using only
generated synthetic data, demonstrating potential for solving downstream tasks.
Project page: https://synth-forge.github.io
