ATLAS: Decoupling Skeletal and Shape Parameters for Expressive Parametric Human Modeling

• URL: http://arxiv.org/abs/2508.15767v1
• Date: Thu, 21 Aug 2025 17:58:56 GMT
• Title: ATLAS: Decoupling Skeletal and Shape Parameters for Expressive Parametric Human Modeling
• Authors: Jinhyung Park, Javier Romero, Shunsuke Saito, Fabian Prada, Takaaki Shiratori, Yichen Xu, Federica Bogo, Shoou-I Yu, Kris Kitani, Rawal Khirodkar,
• Abstract summary: We present ATLAS, a high-fidelity body model learned from 600k high-resolution scans captured using 240 synchronized cameras.<n>We explicitly decouple the shape and skeleton bases by grounding our mesh representation in the human skeleton.<n> ATLAS outperforms existing methods by fitting unseen subjects in diverse poses more accurately, and quantitative evaluations show that our non-linear pose correctives more effectively capture complex poses compared to linear models.
• Score: 43.66748605071065
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Parametric body models offer expressive 3D representation of humans across a wide range of poses, shapes, and facial expressions, typically derived by learning a basis over registered 3D meshes. However, existing human mesh modeling approaches struggle to capture detailed variations across diverse body poses and shapes, largely due to limited training data diversity and restrictive modeling assumptions. Moreover, the common paradigm first optimizes the external body surface using a linear basis, then regresses internal skeletal joints from surface vertices. This approach introduces problematic dependencies between internal skeleton and outer soft tissue, limiting direct control over body height and bone lengths. To address these issues, we present ATLAS, a high-fidelity body model learned from 600k high-resolution scans captured using 240 synchronized cameras. Unlike previous methods, we explicitly decouple the shape and skeleton bases by grounding our mesh representation in the human skeleton. This decoupling enables enhanced shape expressivity, fine-grained customization of body attributes, and keypoint fitting independent of external soft-tissue characteristics. ATLAS outperforms existing methods by fitting unseen subjects in diverse poses more accurately, and quantitative evaluations show that our non-linear pose correctives more effectively capture complex poses compared to linear models.

Related papers

• DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior [82.9526308672547]
  We present DPoser-X, a diffusion-based prior model for 3D whole-body human poses.<n>Our approach unifies various pose-centric tasks as inverse problems, solving them through variational diffusion sampling.<n>Our model consistently outperforms state-of-the-art alternatives, establishing a new benchmark for whole-body human pose prior modeling.
  arXiv  Detail & Related papers  (2025-08-01T12:56:39Z)
• Within the Dynamic Context: Inertia-aware 3D Human Modeling with Pose Sequence [47.16903508897047]
  In this study, we elucidate that variations in human appearance depend not only on the current frame's pose condition but also on past pose states. We introduce Dyco, a novel method utilizing the delta pose sequence representation for non-rigid deformations. In addition, our inertia-aware 3D human method can unprecedentedly simulate appearance changes caused by inertia at different velocities.
  arXiv  Detail & Related papers  (2024-03-28T06:05:14Z)
• Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance [25.346255905155424]
  We introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework. By representing the 3D human parametric model as the motion guidance, we can perform parametric shape alignment of the human body between the reference image and the source video motion. Our approach also exhibits superior generalization capabilities on the proposed in-the-wild dataset.
  arXiv  Detail & Related papers  (2024-03-21T18:52:58Z)
• 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)
• Imposing Temporal Consistency on Deep Monocular Body Shape and Pose Estimation [67.23327074124855]
  This paper presents an elegant solution for the integration of temporal constraints in the fitting process. We derive parameters of a sequence of body models, representing shape and motion of a person, including jaw poses, facial expressions, and finger poses. Our approach enables the derivation of realistic 3D body models from image sequences, including facial expression and articulated hands.
  arXiv  Detail & Related papers  (2022-02-07T11:11:55Z)
• 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)
• imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose [42.4185273307021]
  We present imGHUM, the first holistic generative model of 3D human shape and articulated pose. We model the full human body implicitly as a function zero-level-set and without the use of an explicit template mesh.
  arXiv  Detail & Related papers  (2021-08-24T17:08:28Z)
• Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction [123.62341095156611]
  Implicit functions represented as deep learning approximations are powerful for reconstructing 3D surfaces. Such features are essential in building flexible models for both computer graphics and computer vision. We present methodology that combines detail-rich implicit functions and parametric representations.
  arXiv  Detail & Related papers  (2020-07-22T13:46:14Z)

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.