# Efficient Domain Adaptation via Generative Prior for 3D Infant Pose Estimation

arXiv: https://arxiv.org/abs/2311.12043 · HTML: https://mlsanity.com/paper/2311.12043/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 42 references that are in the map.
- First posted on arXiv 17 Nov 2023.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [SMIL: Multimodal Learning with Severely Missing Modality](https://mlsanity.com/paper/2103.05677/) (2021)
- [Semantic Graph Convolutional Networks for 3D Human Pose Regression](https://mlsanity.com/paper/1904.03345/) (2019)
- [PoseTrack: A Benchmark for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1710.10000/) (2017)
- [Image Generation From Small Datasets via Batch Statistics Adaptation](https://mlsanity.com/paper/1904.01774/) (2019)
- [On Self-Contact and Human Pose](https://mlsanity.com/paper/2104.03176/) (2021)
- [Combining detection and tracking for human pose estimation in videos](https://mlsanity.com/paper/2003.13743/) (2020)
- [DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models](https://mlsanity.com/paper/2302.06826/) (2023)
- [Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation](https://mlsanity.com/paper/2307.03833/) (2023)
- [Invariant Representation Learning for Infant Pose Estimation with Small Data](https://mlsanity.com/paper/2010.06100/) (2020)
- [PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation](https://mlsanity.com/paper/2308.09678/) (2023)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model](https://mlsanity.com/paper/2209.11477/) (2022)
- [Computer Vision to the Rescue: Infant Postural Symmetry Estimation from Incongruent Annotations](https://mlsanity.com/paper/2207.09352/) (2022)
- [MPM: A Unified 2D-3D Human Pose Representation via Masked Pose Modeling](https://mlsanity.com/paper/2306.17201/) (2023)
- [Unsupervised Domain Adaptation Learning for Hierarchical Infant Pose Recognition with Synthetic Data](https://mlsanity.com/paper/2205.01892/) (2022)

## Built on by (most cited first, 8 of 8)

- [RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmark](https://mlsanity.com/paper/2407.13930/) (2024)
- [ChatMotion: A Multimodal Multi-Agent for Human Motion Analysis](https://mlsanity.com/paper/2502.18180/) (2025)
- [RAM: Recover Any 3D Human Motion in-the-Wild](https://mlsanity.com/paper/2603.19929/) (2026)
- [UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning](https://mlsanity.com/paper/2311.16477/) (2023)
- [Comparison of marker-less 2D image-based methods for infant pose estimation](https://mlsanity.com/paper/2410.04980/) (2024)
- [Leveraging Synthetic Adult Datasets for Unsupervised Infant Pose Estimation](https://mlsanity.com/paper/2504.05789/) (2025)
- [CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis](https://mlsanity.com/paper/2510.11233/) (2025)
- [ToddlerAct: A Toddler Action Recognition Dataset for Gross Motor Development Assessment](https://mlsanity.com/paper/2409.00349/) (2024)

## Abstract (arXiv)

Although 3D human pose estimation has gained impressive development in recent years, only a few works focus on infants, that have different bone lengths and also have limited data. Directly applying adult pose estimation models typically achieves low performance in the infant domain and suffers from out-of-distribution issues. Moreover, the limitation of infant pose data collection also heavily constrains the efficiency of learning-based models to lift 2D poses to 3D. To deal with the issues of small datasets, domain adaptation and data augmentation are commonly used techniques. Following this paradigm, we take advantage of an optimization-based method that utilizes generative priors to predict 3D infant keypoints from 2D keypoints without the need of large training data. We further apply a guided diffusion model to domain adapt 3D adult pose to infant pose to supplement small datasets. Besides, we also prove that our method, ZeDO-i, could attain efficient domain adaptation, even if only a small number of data is given. Quantitatively, we claim that our model attains state-of-the-art MPJPE performance of 43.6 mm on the SyRIP dataset and 21.2 mm on the MINI-RGBD dataset.