DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior

• URL: http://arxiv.org/abs/2508.00599v2
• Date: Mon, 04 Aug 2025 04:44:14 GMT
• Title: DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior
• Authors: Junzhe Lu, Jing Lin, Hongkun Dou, Ailing Zeng, Yue Deng, Xian Liu, Zhongang Cai, Lei Yang, Yulun Zhang, Haoqian Wang, Ziwei Liu,
• Abstract summary: 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.
• Score: 82.9526308672547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present DPoser-X, a diffusion-based prior model for 3D whole-body human poses. Building a versatile and robust full-body human pose prior remains challenging due to the inherent complexity of articulated human poses and the scarcity of high-quality whole-body pose datasets. To address these limitations, we introduce a Diffusion model as body Pose prior (DPoser) and extend it to DPoser-X for expressive whole-body human pose modeling. Our approach unifies various pose-centric tasks as inverse problems, solving them through variational diffusion sampling. To enhance performance on downstream applications, we introduce a novel truncated timestep scheduling method specifically designed for pose data characteristics. We also propose a masked training mechanism that effectively combines whole-body and part-specific datasets, enabling our model to capture interdependencies between body parts while avoiding overfitting to specific actions. Extensive experiments demonstrate DPoser-X's robustness and versatility across multiple benchmarks for body, hand, face, and full-body pose modeling. Our model consistently outperforms state-of-the-art alternatives, establishing a new benchmark for whole-body human pose prior modeling.

Related papers

• Multi-modal Pose Diffuser: A Multimodal Generative Conditional Pose Prior [8.314155285516073]
  MOPED is the first method to leverage a novel multi-modal conditional diffusion model as a prior for SMPL pose parameters. Our method offers powerful unconditional pose generation with the ability to condition on multi-modal inputs such as images and text.
  arXiv  Detail & Related papers  (2024-10-18T15:29:19Z)
• MUC: Mixture of Uncalibrated Cameras for Robust 3D Human Body Reconstruction [12.942635715952525]
  Multiple cameras can provide comprehensive multi-view video coverage of a person. Previous studies have overlooked the challenges posed by self-occlusion under multiple views. We introduce a method to reconstruct the 3D human body from multiple uncalibrated camera views.
  arXiv  Detail & Related papers  (2024-03-08T05:03:25Z)
• ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation [71.2556016049579]
  ManiPose is a manifold-constrained multi-hypothesis model for human-pose 2D-to-3D lifting.<n>By constraining the outputs to lie on the human pose manifold, ManiPose guarantees the consistency of all hypothetical poses.<n>We showcase the performance of ManiPose on real-world datasets, where it outperforms state-of-the-art models in pose consistency.
  arXiv  Detail & Related papers  (2023-12-11T13:50:10Z)
• DPoser: Diffusion Model as Robust 3D Human Pose Prior [51.75784816929666]
  We introduce DPoser, a robust and versatile human pose prior built upon diffusion models. DPoser regards various pose-centric tasks as inverse problems and employs variational diffusion sampling for efficient solving. Our approach demonstrates considerable enhancements over common uniform scheduling used in image domains, boasting improvements of 5.4%, 17.2%, and 3.8% across human mesh recovery, pose completion, and motion denoising, respectively.
  arXiv  Detail & Related papers  (2023-12-09T11:18:45Z)
• A generic diffusion-based approach for 3D human pose prediction in the wild [68.00961210467479]
  3D human pose forecasting, i.e., predicting a sequence of future human 3D poses given a sequence of past observed ones, is a challenging-temporal task. We provide a unified formulation in which incomplete elements (no matter in the prediction or observation) are treated as noise and propose a conditional diffusion model that denoises them and forecasts plausible poses. We investigate our findings on four standard datasets and obtain significant improvements over the state-of-the-art.
  arXiv  Detail & Related papers  (2022-10-11T17:59:54Z)
• Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields [47.62275563070933]
  We present a continuous model for plausible human poses based on neural distance fields (NDFs) Pose-NDF learns a manifold of plausible poses as the zero level set of a neural implicit function. It can be used to generate more diverse poses by random sampling and projection than VAE-based methods.
  arXiv  Detail & Related papers  (2022-07-27T21:46:47Z)
• 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)
• 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.