We present MAMMA, a markerless motion-capture pipeline that accurately recovers SMPL-X parameters from multi-view video of two-person interaction sequences. 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 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 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. Our dataset is available in https://mamma.is.tue.mpg.de for research purposes.

翻译：我们提出MAMMA——一种无需标记点的运动捕捉流程，可从双人交互序列的多视角视频中精确恢复SMPL-X参数。传统运动捕捉系统依赖物理标记点，虽能实现高精度，但需专用硬件、人工标记点布设及大量后处理，导致成本高昂且耗时。近期基于学习的方法试图克服这些局限，但大多针对单人捕捉场景，依赖稀疏关键点，或难以应对遮挡与物理交互。本研究提出一种方法，通过分割掩码预测密集的二维接触感知表面地标点，即使在严重遮挡下也能实现逐人员对应估计。我们采用一种利用可学习查询的新颖架构，为各地标点进行显式建模。实验表明，该方法能处理复杂的人际交互，精度优于现有方法。为训练网络，我们构建了大规模合成多视角数据集，融合了包含极端姿态、手部动作及紧密交互在内的多样化人体运动来源。该数据集生成具有丰富身体接触与遮挡的高变异性合成序列，并提供包含密集二维地标点的SMPL-X真值标注。最终形成的系统可在无标记点条件下捕捉人体运动，其重建质量可与商用基于标记点的运动捕捉方案相媲美，且无需大量手动清理。此外，针对密集地标点预测与无标记运动捕捉领域缺乏通用基准的问题，我们基于真实多视角序列构建了两项评估设置。数据集已在https://mamma.is.tue.mpg.de公开，供研究使用。