Multi-View 3D Human Pose Estimation with Weakly Synchronized Images

Abstract

Multi-view 3D human pose estimation (MHPE) is an important research task in computer vision. To maintain consistency during the data collection, hardware synchronization devices are commonly used to connect cameras, ensuring that images from different views are captured simultaneously. However, synchronizing with extra devices has two apparent limitations: the hardware is i) usually expensive and ii) less flexible for deployment in outdoor open scenarios. Suppose the model can improve its tolerance for the time differences in multi-view image capture. In that case, the difficulty and cost of deployment will be greatly reduced, and MHPE will become more widespread. In this paper, we try to answer how to build a model that performs pose estimation directly using ''weakly synchronized images" from multiple views, where the captured images shift from each other within a frame. To this end, we introduce a new multi-view 3D human pose estimation task given weakly synchronized image inputs. Apart from existing well-synchronized datasets, we present the first weakly synchronized dataset comprising 800k images. Thereon, we propose SyncDiffPose, a novel model based on the diffusion method for pose estimation to denoise the error in such data. By combining simple synchronization strategies, e.g., the timer method, our approach can perform pose estimation without hardware calibration.