BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion

TL;DR

This paper introduces BEDLAM, a highly realistic synthetic dataset for 3D human pose and shape estimation, demonstrating that models trained solely on this synthetic data can achieve state-of-the-art results on real images.

Contribution

The creation of BEDLAM, a comprehensive synthetic dataset with realistic clothing and motion, and its use to train models that outperform previous methods on real-world benchmarks.

Findings

Models trained on BEDLAM achieve state-of-the-art accuracy on real benchmarks.

Synthetic data can rival real data for training high-performance 3D human pose models.

Realistic clothing simulation enhances the utility of synthetic datasets.

Abstract

We show, for the first time, that neural networks trained only on synthetic data achieve state-of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real images. Previous synthetic datasets have been small, unrealistic, or lacked realistic clothing. Achieving sufficient realism is non-trivial and we show how to do this for full bodies in motion. Specifically, our BEDLAM dataset contains monocular RGB videos with ground-truth 3D bodies in SMPL-X format. It includes a diversity of body shapes, motions, skin tones, hair, and clothing. The clothing is realistically simulated on the moving bodies using commercial clothing physics simulation. We render varying numbers of people in realistic scenes with varied lighting and camera motions. We then train various HPS regressors using BEDLAM and achieve state-of-the-art accuracy on real-image benchmarks despite training with synthetic data. We use BEDLAM to gain insights into what model design choices are important for accuracy. With good synthetic training data, we find that a basic method like HMR approaches the accuracy of the current SOTA method (CLIFF). BEDLAM is useful for a variety of tasks and all images, ground truth bodies, 3D clothing, support code, and more are available for research purposes. Additionally, we provide detailed information about our synthetic data generation pipeline, enabling others to generate their own datasets. See the project page: https://bedlam.is.tue.mpg.de/.