title: Reconstructing Humans with a Biomechanically Accurate Skeleton
summary: In this paper, we introduce a method for reconstructing 3D humans from a
  single image using a biomechanically accurate skeleton model. To achieve this,
  we train a transformer that takes an image as input and estimates the
  parameters of the model. Due to the lack of training data for this task, we
  build a pipeline to produce pseudo ground truth model parameters for single
  images and implement a training procedure that iteratively refines these pseudo
  labels. Compared to state-of-the-art methods for 3D human mesh recovery, our
  model achieves competitive performance on standard benchmarks, while it
  significantly outperforms them in settings with extreme 3D poses and
  viewpoints. Additionally, we show that previous reconstruction methods
  frequently violate joint angle limits, leading to unnatural rotations. In
  contrast, our approach leverages the biomechanically plausible degrees of
  freedom making more realistic joint rotation estimates. We validate our
  approach across multiple human pose estimation benchmarks. We make the code,
  models and data available at: https://isshikihugh.github.io/HSMR/
paper_authors._id: 67e6d76a3394f1ed9c9d804b
paper_authors.name: Yan Xia
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-31T08:14:23.016Z
paper_authors._id: 66e1103cde9aca0f831f05d8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66e1103cde9aca0f831f05d8/tkdrZgBH9Kyxv_VrGK05R.png
paper_authors.fullname: Yan XIA
paper_authors.type: user
paper_authors.user: IsshikiHugh
paper_authors._id: 67e6d76a3394f1ed9c9d804c
paper_authors.name: Xiaowei Zhou
paper_authors._id: 67e6d76a3394f1ed9c9d804d
paper_authors.name: Etienne Vouga
paper_authors._id: 67e6d76a3394f1ed9c9d804e
paper_authors.name: Qixing Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:57:32.624Z
paper_authors._id: 67f18d051358145ee5bb3a11
paper_authors.avatarUrl: /avatars/e5a8c12295d7609625c2b891d47c3112.svg
paper_authors.fullname: qixing huang
paper_authors.type: user
paper_authors.user: qixinghuang1
paper_authors._id: 67e6d76a3394f1ed9c9d804f
paper_authors.name: Georgios Pavlakos
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T10:03:56.262Z
paper_authors._id: 6478d3433b7f8b1f6249b469
paper_authors.avatarUrl: /avatars/11e7d7a94ae26500c1c2ad62e760726f.svg
paper_authors.fullname: Georgios Pavlakos
paper_authors.type: user
paper_authors.user: geopavlakos
paper_title: Reconstructing Humans with a Biomechanically Accurate Skeleton
paper_summary: In this paper, we introduce a method for reconstructing 3D humans from a
  single image using a biomechanically accurate skeleton model. To achieve this,
  we train a transformer that takes an image as input and estimates the
  parameters of the model. Due to the lack of training data for this task, we
  build a pipeline to produce pseudo ground truth model parameters for single
  images and implement a training procedure that iteratively refines these pseudo
  labels. Compared to state-of-the-art methods for 3D human mesh recovery, our
  model achieves competitive performance on standard benchmarks, while it
  significantly outperforms them in settings with extreme 3D poses and
  viewpoints. Additionally, we show that previous reconstruction methods
  frequently violate joint angle limits, leading to unnatural rotations. In
  contrast, our approach leverages the biomechanically plausible degrees of
  freedom making more realistic joint rotation estimates. We validate our
  approach across multiple human pose estimation benchmarks. We make the code,
  models and data available at: https://isshikihugh.github.io/HSMR/
paper_ai_summary: A transformer-based method for reconstructing 3D humans from a single image using a biomechanically accurate skeleton model achieves competitive performance and more realistic joint rotations compared to state-of-the-art methods.
paper_ai_keywords: transformer biomechanically accurate skeleton model pseudo ground truth model parameters iterative refinement 3D human mesh recovery joint angle limits human pose estimation benchmarks