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EgoHumans: An Egocentric 3D Multi-Human Benchmark

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arxiv 2305.16487 v2 pith:3T2MLMMX submitted 2023-05-25 cs.CV cs.AI

EgoHumans: An Egocentric 3D Multi-Human Benchmark

classification cs.CV cs.AI
keywords egocentricmulti-humanbenchmarkcaptureegohumanshumanposetracking
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present EgoHumans, a new multi-view multi-human video benchmark to advance the state-of-the-art of egocentric human 3D pose estimation and tracking. Existing egocentric benchmarks either capture single subject or indoor-only scenarios, which limit the generalization of computer vision algorithms for real-world applications. We propose a novel 3D capture setup to construct a comprehensive egocentric multi-human benchmark in the wild with annotations to support diverse tasks such as human detection, tracking, 2D/3D pose estimation, and mesh recovery. We leverage consumer-grade wearable camera-equipped glasses for the egocentric view, which enables us to capture dynamic activities like playing tennis, fencing, volleyball, etc. Furthermore, our multi-view setup generates accurate 3D ground truth even under severe or complete occlusion. The dataset consists of more than 125k egocentric images, spanning diverse scenes with a particular focus on challenging and unchoreographed multi-human activities and fast-moving egocentric views. We rigorously evaluate existing state-of-the-art methods and highlight their limitations in the egocentric scenario, specifically on multi-human tracking. To address such limitations, we propose EgoFormer, a novel approach with a multi-stream transformer architecture and explicit 3D spatial reasoning to estimate and track the human pose. EgoFormer significantly outperforms prior art by 13.6% IDF1 on the EgoHumans dataset.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. LAMP: Localization Aware Multi-camera People Tracking in Metric 3D World

    cs.CV 2026-05 unverdicted novelty 7.0

    LAMP tracks 3D human motion from moving multi-camera headsets by converting 2D detections to a unified metric 3D world frame via device localization and fitting with an end-to-end spatio-temporal transformer.

  2. CoMind: Understanding Collaborative Human Activity from Multiple Minds and Views

    cs.CV 2026-07 accept novelty 6.5

    CoMind releases 41 h of synchronized multi-view cooking collaboration with social-cue annotations and three ToM-oriented benchmarks on which current VLMs score poorly until fine-tuned.

  3. MoViD: View-Invariant 3D Human Pose Estimation via Motion-View Disentanglement

    cs.CV 2026-03 unverdicted novelty 6.0

    MoViD disentangles motion and view features via a view estimator and orthogonal projection with contrastive alignment to deliver viewpoint-invariant 3D pose estimation that cuts errors over 24% with 60% less data and ...