iMapper: Interaction-guided Joint Scene and Human Motion Mapping from Monocular Videos

TL;DR

iMapper is a data-driven approach that jointly recovers scene arrangements and human interactions from monocular videos by exploiting the correlation between human motions and object configurations, especially under occlusion.

Contribution

The paper introduces iMapper, a novel method that jointly reasons about human-object interactions and scene layout from monocular videos, improving robustness under occlusion.

Findings

Significantly outperforms state-of-the-art methods in occluded scenarios.

Effectively recovers plausible scene arrangements and human motions.

Demonstrates robustness to moderate and heavy occlusion.

Abstract

A long-standing challenge in scene analysis is the recovery of scene arrangements under moderate to heavy occlusion, directly from monocular video. While the problem remains a subject of active research, concurrent advances have been made in the context of human pose reconstruction from monocular video, including image-space feature point detection and 3D pose recovery. These methods, however, start to fail under moderate to heavy occlusion as the problem becomes severely under-constrained. We approach the problems differently. We observe that people interact similarly in similar scenes. Hence, we exploit the correlation between scene object arrangement and motions performed in that scene in both directions: first, typical motions performed when interacting with objects inform us about possible object arrangements; and second, object arrangements, in turn, constrain the possible motions. We present iMapper, a data-driven method that focuses on identifying human-object interactions, and jointly reasons about objects and human movement over space-time to recover both a plausible scene arrangement and consistent human interactions. We first introduce the notion of characteristic interactions as regions in space-time when an informative human-object interaction happens. This is followed by a novel occlusion-aware matching procedure that searches and aligns such characteristic snapshots from an interaction database to best explain the input monocular video. Through extensive evaluations, both quantitative and qualitative, we demonstrate that iMapper significantly improves performance over both dedicated state-of-the-art scene analysis and 3D human pose recovery approaches, especially under medium to heavy occlusion.