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This article aims to use graphic engines to simulate a large number oftraining data that have free annotations and possibly strongly resemble toreal-world data. Between synthetic and real, a two-level domain gap exists,involving content level and appearance level. While the latter is concernedwith appearance style, the former problem arises from a different mechanism,i.e, content mismatch in attributes such as camera viewpoint, object placementand lighting conditions. In contrast to the widely-studied appearance-levelgap, the content-level discrepancy has not been broadly studied. To address thecontent-level misalignment, we propose an attribute descent approach thatautomatically optimizes engine attributes to enable synthetic data toapproximate real-world data. We verify our method on object-centric tasks,wherein an object takes up a major portion of an image. In these tasks, thesearch space is relatively small, and the optimization of each attribute yieldssufficiently obvious supervision signals. We collect a new synthetic assetVehicleX, and reformat and reuse existing the synthetic assets ObjectX andPersonX. Extensive experiments on image classification and objectre-identification confirm that adapted synthetic data can be effectively usedin three scenarios: training with synthetic data only, training dataaugmentation and numerically understanding dataset content.

Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond