PoseKernelLifter: Metric Lifting of 3D Human Pose using Sound

TL;DR

This paper introduces PoseKernelLifter, a method that uses audio signals along with visual data to accurately reconstruct 3D human poses in metric scale from a single image, addressing a key challenge in pose estimation.

Contribution

It proposes a novel audio-based pose kernel and a multi-modal CNN to achieve metric 3D pose reconstruction, outperforming existing methods.

Findings

Achieves accurate metric 3D pose reconstruction in real scenes.

Outperforms state-of-the-art methods in scale accuracy.

Introduces a generalizable pose kernel invariant to scene changes.

Abstract

Reconstructing the 3D pose of a person in metric scale from a single view image is a geometrically ill-posed problem. For example, we can not measure the exact distance of a person to the camera from a single view image without additional scene assumptions (e.g., known height). Existing learning based approaches circumvent this issue by reconstructing the 3D pose up to scale. However, there are many applications such as virtual telepresence, robotics, and augmented reality that require metric scale reconstruction. In this paper, we show that audio signals recorded along with an image, provide complementary information to reconstruct the metric 3D pose of the person. The key insight is that as the audio signals traverse across the 3D space, their interactions with the body provide metric information about the body's pose. Based on this insight, we introduce a time-invariant transfer function called pose kernel -- the impulse response of audio signals induced by the body pose. The main properties of the pose kernel are that (1) its envelope highly correlates with 3D pose, (2) the time response corresponds to arrival time, indicating the metric distance to the microphone, and (3) it is invariant to changes in the scene geometry configurations. Therefore, it is readily generalizable to unseen scenes. We design a multi-stage 3D CNN that fuses audio and visual signals and learns to reconstruct 3D pose in a metric scale. We show that our multi-modal method produces accurate metric reconstruction in real world scenes, which is not possible with state-of-the-art lifting approaches including parametric mesh regression and depth regression.