# Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences

arXiv: https://arxiv.org/abs/1810.07538 · HTML: https://mlsanity.com/paper/1810.07538/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2018.

## Built on by (most cited first, 19 of 19)

- [Humans in 4D: Reconstructing and Tracking Humans with Transformers](https://mlsanity.com/paper/2305.20091/) (2023)
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- [imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose](https://mlsanity.com/paper/2108.10842/) (2021)
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- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [Markerless human pose estimation for biomedical applications: a survey](https://mlsanity.com/paper/2308.00519/) (2023)
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- [VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions](https://mlsanity.com/paper/2506.23236/) (2025)
- [In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains](https://mlsanity.com/paper/2111.15124/) (2021)
- [Degrees of Freedom Matter: Inferring Dynamics from Point Trajectories](https://mlsanity.com/paper/2406.03625/) (2024)
- [Heuristic Weakly Supervised 3D Human Pose Estimation](https://mlsanity.com/paper/2105.10996/) (2021)
- [Video-Based Inpatient Fall Risk Assessment: A Case Study](https://mlsanity.com/paper/2106.07565/) (2021)
- [Automatic Assessment of Infant Face and Upper-Body Symmetry as Early Signs of Torticollis](https://mlsanity.com/paper/2210.15022/) (2022)
- [Infant movement classification through pressure distribution analysis](https://mlsanity.com/paper/2208.00884/) (2022)
- [Fetuses Made Simple: Modeling and Tracking of Fetal Shape and Pose](https://mlsanity.com/paper/2506.17858/) (2025)
- [Markerless Motion Capture for Biomechanical Whole-Body Kinematic Estimation in Infants](https://mlsanity.com/paper/2605.17120/) (2026)
- [Aligning Fetal Anatomy with Kinematic Tree Log-Euclidean PolyRigid Transforms](https://mlsanity.com/paper/2603.02371/) (2026)
- [Cross-Model Distillation of a Human-Pose Foundation Model from Unannotated Infant Video for Markerless 3D Pose Estimation](https://mlsanity.com/paper/2609.01840/) (2026)

## Abstract (arXiv)

Statistical models of the human body surface are generally learned from thousands of high-quality 3D scans in predefined poses to cover the wide variety of human body shapes and articulations. Acquisition of such data requires expensive equipment, calibration procedures, and is limited to cooperative subjects who can understand and follow instructions, such as adults. We present a method for learning a statistical 3D Skinned Multi-Infant Linear body model (SMIL) from incomplete, low-quality RGB-D sequences of freely moving infants. Quantitative experiments show that SMIL faithfully represents the RGB-D data and properly factorizes the shape and pose of the infants. To demonstrate the applicability of SMIL, we fit the model to RGB-D sequences of freely moving infants and show, with a case study, that our method captures enough motion detail for General Movements Assessment (GMA), a method used in clinical practice for early detection of neurodevelopmental disorders in infants. SMIL provides a new tool for analyzing infant shape and movement and is a step towards an automated system for GMA.