Spatiotemporal Bundle Adjustment for Dynamic 3D Human Reconstruction in the Wild

TL;DR

This paper introduces a novel spatiotemporal bundle adjustment framework that jointly optimizes camera parameters, static and dynamic 3D points, and temporal alignment to improve 3D human motion reconstruction from multiple unsynchronized videos in the wild.

Contribution

It presents a joint optimization method incorporating physics-based motion priors for dynamic scenes, with an efficient incremental algorithm for high-accuracy 3D human motion reconstruction.

Findings

Enhanced 3D motion trajectory accuracy in wild scenarios

Sub-frame temporal alignment improves reconstruction resolution

Significant benefits in human body model fitting accuracy

Abstract

Bundle adjustment jointly optimizes camera intrinsics and extrinsics and 3D point triangulation to reconstruct a static scene. The triangulation constraint, however, is invalid for moving points captured in multiple unsynchronized videos and bundle adjustment is not designed to estimate the temporal alignment between cameras. We present a spatiotemporal bundle adjustment framework that jointly optimizes four coupled sub-problems: estimating camera intrinsics and extrinsics, triangulating static 3D points, as well as sub-frame temporal alignment between cameras and computing 3D trajectories of dynamic points. Key to our joint optimization is the careful integration of physics-based motion priors within the reconstruction pipeline, validated on a large motion capture corpus of human subjects. We devise an incremental reconstruction and alignment algorithm to strictly enforce the motion prior during the spatiotemporal bundle adjustment. This algorithm is further made more efficient by a divide and conquer scheme while still maintaining high accuracy. We apply this algorithm to reconstruct 3D motion trajectories of human bodies in dynamic events captured by multiple uncalibrated and unsynchronized video cameras in the wild. To make the reconstruction visually more interpretable, we fit a statistical 3D human body model to the asynchronous video streams.Compared to the baseline, the fitting significantly benefits from the proposed spatiotemporal bundle adjustment procedure. Because the videos are aligned with sub-frame precision, we reconstruct 3D motion at much higher temporal resolution than the input videos.