Reconstructing Humans with a Biomechanically Accurate Skeleton
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/
67e6d76a3394f1ed9c9d804b
Yan Xia
claimed_verified
2025-03-31T08:14:23.016Z
66e1103cde9aca0f831f05d8
https://cdn-avatars.huggingface.co/v1/production/uploads/66e1103cde9aca0f831f05d8/tkdrZgBH9Kyxv_VrGK05R.png
Yan XIA
user
IsshikiHugh
67e6d76a3394f1ed9c9d804c
Xiaowei Zhou
67e6d76a3394f1ed9c9d804d
Etienne Vouga
67e6d76a3394f1ed9c9d804e
Qixing Huang
claimed_verified
2025-04-08T06:57:32.624Z
67f18d051358145ee5bb3a11
/avatars/e5a8c12295d7609625c2b891d47c3112.svg
qixing huang
user
qixinghuang1
67e6d76a3394f1ed9c9d804f
Georgios Pavlakos
admin_assigned
2025-03-31T10:03:56.262Z
6478d3433b7f8b1f6249b469
/avatars/11e7d7a94ae26500c1c2ad62e760726f.svg
Georgios Pavlakos
user
geopavlakos
Reconstructing Humans with a Biomechanically Accurate Skeleton
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/
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.
transformer biomechanically accurate skeleton model pseudo ground truth model parameters iterative refinement 3D human mesh recovery joint angle limits human pose estimation benchmarks