Positive users praise MPI Lab's MAMMA markerless mocap system for iPhones because it generalizes well to multiple interacting people, includes open research code to democratize access, and comes from an amazing team.
Try it and capture lots of data. Let us know how it goes. MAMMA is the result of an amazing team: @Hanzcun, @soyong_shin, @TsvetelinaAlex2, @AYiannakidis, @gfgbec, Markus Höschle, Joachim Tesch & Taylor Obersat.
Project: https://mamma.is.tue.mpg.de/ Code: https://github.com/cuevhv/mamma Paper: https://arxiv.org/abs/2506.13040
The approach is trained on the 2-person case but generalizes well to more people interacting closely.
We make the code available for research and hope it will democratize mocap — the world needs more 3D motions. Commercial licensing is also possible.
The key idea is to train a network to estimate dense keypoints on the surface of the body. These are like virtual mocap markers. Our network architecture uses per-landmark learnable tokens, which are key to accuracy.
To make this robust to multi-person occlusion and hand motion, we created a synthetic dataset that we use to train it. The network is trained to predict occlusion and contact probabilities.