← 研踪 PaperTrailCVF · CVPR 2026:cvf:42b1a3e5e491663df5408b75cdb5bec0 · 记录 #1
MAMMA: Markerless Accurate Multi-person Motion Acquisition
Velasquez, Hanz CuevasYiannakidis, AnastasiosShin, SoyongBecherini, GiorgioHöschle, MarkusTesch, JoachimObersat, TaylorAlexiadis, TsvetelinaHalilaj, EniBlack, Michael J. 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 21:49
摘要
We present MAMMA, a markerless motion-capture pipeline that accurately recovers SMPL-X parameters from multi-view video. Traditional motion-capture systems rely on physical markers. Although they offer high accuracy, their requirements of specialized hardware, manual marker placement, and extensive post-processing make them costly and time-consuming. Recent learning-based methods attempt to overcome these limitations, but most are designed for single-person capture, rely on sparse keypoints, or struggle with occlusions and physical interactions. In this work, we introduce a method that predicts dense 2D contact-aware and visibility-aware surface landmarks conditioned on segmentation masks, enabling person-specific correspondence estimation even under heavy occlusion. We employ a novel architecture that exploits learnable queries for each landmark. We demonstrate that our approach can handle complex person--person interaction and offers greater accuracy than existing methods. To train our network, we construct a large, synthetic multi-view dataset combining human motions from diverse sources, including extreme poses, hand motions, and close interactions. Our dataset yields high-variability synthetic sequences with rich body contact and occlusion, and includes SMPL-X ground-truth annotations with dense 2D landmarks. The result is a system capable of accurately capturing human motion without the need for markers. Our approach offers competitive reconstruction quality compared to commercial marker-based motion-capture solutions, without the extensive manual cleanup. Finally, we address the absence of common benchmarks for dense-landmark prediction and markerless motion capture by introducing two evaluation settings built from real multi-view sequences. https://mamma.is.tue.mpg.de/
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记录 #1 · 2026 · 最新观测版本
MAMMA: Markerless Accurate Multi-person Motion Acquisition
We present MAMMA, a markerless motion-capture pipeline that accurately recovers SMPL-X parameters from multi-view video. Traditional motion-capture systems rely on physical markers. Although they offer high accuracy, their requirements of specialized hardware, manual marker placement, and extensive post-processing make them costly and time-consuming. Recent learning-based methods attempt to overcome these limitations, but most are designed for single-person capture, rely on sparse keypoints, or struggle with occlusions and physical interactions. In this work, we introduce a method that predicts dense 2D contact-aware and visibility-aware surface landmarks conditioned on segmentation masks, enabling person-specific correspondence estimation even under heavy occlusion. We employ a novel architecture that exploits learnable queries for each landmark. We demonstrate that our approach can handle complex person--person interaction and offers greater accuracy than existing methods. To train our network, we construct a large, synthetic multi-view dataset combining human motions from diverse sources, including extreme poses, hand motions, and close interactions. Our dataset yields high-variability synthetic sequences with rich body contact and occlusion, and includes SMPL-X ground-truth annotations with dense 2D landmarks. The result is a system capable of accurately capturing human motion without the need for markers. Our approach offers competitive reconstruction quality compared to commercial marker-based motion-capture solutions, without the extensive manual cleanup. Finally, we address the absence of common benchmarks for dense-landmark prediction and markerless motion capture by introducing two evaluation settings built from real multi-view sequences. https://mamma.is.tue.mpg.de/
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