Toward Human Understanding with Controllable Synthesis

TL;DR

This paper introduces a controllable synthesis method that balances realism and ground truth accuracy in generated images, improving training data quality for 3D human pose and shape estimation.

Contribution

It presents a novel approach combining generative models with traditional graphics to produce realistic yet accurate training data for HPS estimation.

Findings

Generated images with controlled realism improve HPS accuracy.

Balancing realism and ground truth is crucial for effective training data.

The proposed method outperforms purely synthetic or purely realistic approaches.

Abstract

Training methods to perform robust 3D human pose and shape (HPS) estimation requires diverse training images with accurate ground truth. While BEDLAM demonstrates the potential of traditional procedural graphics to generate such data, the training images are clearly synthetic. In contrast, generative image models produce highly realistic images but without ground truth. Putting these methods together seems straightforward: use a generative model with the body ground truth as controlling signal. However, we find that, the more realistic the generated images, the more they deviate from the ground truth, making them inappropriate for training and evaluation. Enhancements of realistic details, such as clothing and facial expressions, can lead to subtle yet significant deviations from the ground truth, potentially misleading training models. We empirically verify that this misalignment causes the accuracy of HPS networks to decline when trained with generated images. To address this, we design a controllable synthesis method that effectively balances image realism with precise ground truth. We use this to create the Generative BEDLAM (Gen-B) dataset, which improves the realism of the existing synthetic BEDLAM dataset while preserving ground truth accuracy. We perform extensive experiments, with various noise-conditioning strategies, to evaluate the tradeoff between visual realism and HPS accuracy. We show, for the first time, that generative image models can be controlled by traditional graphics methods to produce training data that increases the accuracy of HPS methods.