Age-Inclusive 3D Human Mesh Recovery for Action-Preserving Data Anonymization

• URL: http://arxiv.org/abs/2512.05259v1
• Date: Thu, 04 Dec 2025 21:23:04 GMT
• Title: Age-Inclusive 3D Human Mesh Recovery for Action-Preserving Data Anonymization
• Authors: Georgios Chatzichristodoulou, Niki Efthymiou, Panagiotis Filntisis, Georgios Pavlakos, Petros Maragos,
• Abstract summary: AionHMR is a comprehensive framework designed to bridge the 3D shape and pose estimation domain gap.<n>We propose an optimization-based method that extends a top-performing model by incorporating the SMPL-A body model.<n>We then developed and trained a specialized transformer-based deep learning model capable of real-time 3D age-inclusive human reconstruction.
• Score: 30.818455306299455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While three-dimensional (3D) shape and pose estimation is a highly researched area that has yielded significant advances, the resulting methods, despite performing well for the adult population, generally fail to generalize effectively to children and infants. This paper addresses this challenge by introducing AionHMR, a comprehensive framework designed to bridge this domain gap. We propose an optimization-based method that extends a top-performing model by incorporating the SMPL-A body model, enabling the concurrent and accurate modeling of adults, children, and infants. Leveraging this approach, we generated pseudo-ground-truth annotations for publicly available child and infant image databases. Using these new training data, we then developed and trained a specialized transformer-based deep learning model capable of real-time 3D age-inclusive human reconstruction. Extensive experiments demonstrate that our methods significantly improve shape and pose estimation for children and infants without compromising accuracy on adults. Importantly, our reconstructed meshes serve as privacy-preserving substitutes for raw images, retaining essential action, pose, and geometry information while enabling anonymized datasets release. As a demonstration, we introduce the 3D-BabyRobot dataset, a collection of action-preserving 3D reconstructions of children interacting with robots. This work bridges a crucial domain gap and establishes a foundation for inclusive, privacy-aware, and age-diverse 3D human modeling.

Related papers

• Extending 3D body pose estimation for robotic-assistive therapies of autistic children [4.751886527142779]
  We develop a 3D pose estimator for children with Autism. Our method has an error below $0.3m$, which is considered acceptable for this kind of application. In real-world settings, the proposed model performs similarly to a Kinect depth camera.
  arXiv  Detail & Related papers  (2024-02-12T19:11:03Z)
• Efficient Domain Adaptation via Generative Prior for 3D Infant Pose Estimation [29.037799937729687]
  3D human pose estimation has gained impressive development in recent years, but only a few works focus on infants, that have different bone lengths and also have limited data. Here, we show 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. We also prove that our method, ZeDO-i, could attain efficient domain adaptation, even if only a small number of data is given.
  arXiv  Detail & Related papers  (2023-11-17T20:49:37Z)
• Unsupervised 3D Pose Estimation with Non-Rigid Structure-from-Motion Modeling [83.76377808476039]
  We propose a new modeling method for human pose deformations and design an accompanying diffusion-based motion prior. Inspired by the field of non-rigid structure-from-motion, we divide the task of reconstructing 3D human skeletons in motion into the estimation of a 3D reference skeleton. A mixed spatial-temporal NRSfMformer is used to simultaneously estimate the 3D reference skeleton and the skeleton deformation of each frame from 2D observations sequence.
  arXiv  Detail & Related papers  (2023-08-18T16:41:57Z)
• 3D Reconstruction of Sculptures from Single Images via Unsupervised Domain Adaptation on Implicit Models [11.647208461719906]
  We propose an unsupervised 3D domain adaptation method for adapting a single-view 3D implicit reconstruction model from the source (real-world humans) to the target (sculptures) domain. We have compared the generated shapes with other methods and conducted ablation studies as well as a user study to demonstrate the effectiveness of our adaptation method.
  arXiv  Detail & Related papers  (2022-10-09T13:48:00Z)
• Recovering 3D Human Mesh from Monocular Images: A Survey [49.00136388529404]
  Estimating human pose and shape from monocular images is a long-standing problem in computer vision. This survey focuses on the task of monocular 3D human mesh recovery.
  arXiv  Detail & Related papers  (2022-03-03T18:56:08Z)
• LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies [78.17425779503047]
  We propose a novel neural implicit representation for the human body. It is fully differentiable and optimizable with disentangled shape and pose latent spaces. Our model can be trained and fine-tuned directly on non-watertight raw data with well-designed losses.
  arXiv  Detail & Related papers  (2021-11-30T04:10:57Z)
• THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers [67.8628917474705]
  THUNDR is a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people. We show state-of-the-art results on Human3.6M and 3DPW, for both the fully-supervised and the self-supervised models. We observe very solid 3d reconstruction performance for difficult human poses collected in the wild.
  arXiv  Detail & Related papers  (2021-06-17T09:09:24Z)
• Invariant Representation Learning for Infant Pose Estimation with Small Data [14.91506452479778]
  We release a hybrid synthetic and real infant pose dataset with small yet diverse real images as well as generated synthetic infant poses. In our ablation study, with identical network structure, models trained on SyRIP dataset show noticeable improvement over the ones trained on the only other public infant pose datasets. One of our best infant pose estimation performers on the state-of-the-art DarkPose model shows mean average precision (mAP) of 93.6.
  arXiv  Detail & Related papers  (2020-10-13T01:10:14Z)
• Multi-View Consistency Loss for Improved Single-Image 3D Reconstruction of Clothed People [36.30755368202957]
  We present a novel method to improve the accuracy of the 3D reconstruction of clothed human shape from a single image. The accuracy and completeness for reconstruction of clothed people is limited due to the large variation in shape resulting from clothing, hair, body size, pose and camera viewpoint.
  arXiv  Detail & Related papers  (2020-09-29T17:18:00Z)
• Neural Descent for Visual 3D Human Pose and Shape [67.01050349629053]
  We present deep neural network methodology to reconstruct the 3d pose and shape of people, given an input RGB image. We rely on a recently introduced, expressivefull body statistical 3d human model, GHUM, trained end-to-end. Central to our methodology, is a learning to learn and optimize approach, referred to as HUmanNeural Descent (HUND), which avoids both second-order differentiation.
  arXiv  Detail & Related papers  (2020-08-16T13:38:41Z)
• Cascaded deep monocular 3D human pose estimation with evolutionary training data [76.3478675752847]
  Deep representation learning has achieved remarkable accuracy for monocular 3D human pose estimation. This paper proposes a novel data augmentation method that is scalable for massive amount of training data. Our method synthesizes unseen 3D human skeletons based on a hierarchical human representation and synthesizings inspired by prior knowledge.
  arXiv  Detail & Related papers  (2020-06-14T03:09:52Z)
• Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis [72.34794624243281]
  We propose a self-supervised learning framework to disentangle variations from unlabeled video frames. Our differentiable formalization, bridging the representation gap between the 3D pose and spatial part maps, allows us to operate on videos with diverse camera movements.
  arXiv  Detail & Related papers  (2020-04-09T07:55:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.