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.
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建立在真实数据基础上的生成模型能够以可控的方式呈现逼真的数据，但由于缺乏三维一致性注释和生成样本分布受限等问题，使用这些生成模型生成的数据进行下游任务训练仍未得到充分探索。为了解决这些挑战，我们从一个现有的可控生成模型中提取三维一致性注释，使数据在下游任务中发挥作用，实验证明使用仅生成的合成数据可以与最先进的模型竞争，具有解决下游任务的潜力。