A Simple Strategy for Body Estimation from Partial-View Images
- URL: http://arxiv.org/abs/2404.09301v2
- Date: Tue, 16 Apr 2024 03:27:00 GMT
- Title: A Simple Strategy for Body Estimation from Partial-View Images
- Authors: Yafei Mao, Xuelu Li, Brandon Smith, Jinjin Li, Raja Bala,
- Abstract summary: Virtual try-on and product personalization have become increasingly important in modern online shopping, highlighting the need for accurate body measurement estimation.
Previous research has advanced in estimating 3D body shapes from RGB images, but the task is inherently ambiguous as the observed scale of human subjects in the images depends on two unknown factors: capture distance and body dimensions.
We propose a modular and simple height normalization solution, which relocates the subject skeleton to the desired position, normalizing the scale and disentangling the relationship between the two variables.
- Score: 8.05538560322898
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Virtual try-on and product personalization have become increasingly important in modern online shopping, highlighting the need for accurate body measurement estimation. Although previous research has advanced in estimating 3D body shapes from RGB images, the task is inherently ambiguous as the observed scale of human subjects in the images depends on two unknown factors: capture distance and body dimensions. This ambiguity is particularly pronounced in partial-view scenarios. To address this challenge, we propose a modular and simple height normalization solution. This solution relocates the subject skeleton to the desired position, thereby normalizing the scale and disentangling the relationship between the two variables. Our experimental results demonstrate that integrating this technique into state-of-the-art human mesh reconstruction models significantly enhances partial body measurement estimation. Additionally, we illustrate the applicability of this approach to multi-view settings, showcasing its versatility.
Related papers
- Towards Robust and Expressive Whole-body Human Pose and Shape Estimation [51.457517178632756]
Whole-body pose and shape estimation aims to jointly predict different behaviors of the entire human body from a monocular image.
Existing methods often exhibit degraded performance under the complexity of in-the-wild scenarios.
We propose a novel framework to enhance the robustness of whole-body pose and shape estimation.
arXiv Detail & Related papers (2023-12-14T08:17:42Z) - GS-Pose: Category-Level Object Pose Estimation via Geometric and
Semantic Correspondence [5.500735640045456]
Category-level pose estimation is a challenging task with many potential applications in computer vision and robotics.
We propose to utilize both geometric and semantic features obtained from a pre-trained foundation model.
This requires significantly less data to train than prior methods since the semantic features are robust to object texture and appearance.
arXiv Detail & Related papers (2023-11-23T02:35:38Z) - Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh
Reconstruction [66.10717041384625]
Zolly is the first 3DHMR method focusing on perspective-distorted images.
We propose a new camera model and a novel 2D representation, termed distortion image, which describes the 2D dense distortion scale of the human body.
We extend two real-world datasets tailored for this task, all containing perspective-distorted human images.
arXiv Detail & Related papers (2023-03-24T04:22:41Z) - Estimation of 3D Body Shape and Clothing Measurements from Frontal- and
Side-view Images [8.107762252448195]
estimation of 3D human body shape and clothing measurements is crucial for virtual try-on and size recommendation problems in the fashion industry.
Existing works proposed various solutions to these problems but could not succeed in the industry adaptation because of complexity and restrictions.
We propose a simple yet effective architecture to estimate both shape and measures from frontal- and side-view images.
arXiv Detail & Related papers (2022-05-28T06:10:41Z) - Generalizable Neural Performer: Learning Robust Radiance Fields for
Human Novel View Synthesis [52.720314035084215]
This work targets at using a general deep learning framework to synthesize free-viewpoint images of arbitrary human performers.
We present a simple yet powerful framework, named Generalizable Neural Performer (GNR), that learns a generalizable and robust neural body representation.
Experiments on GeneBody-1.0 and ZJU-Mocap show better robustness of our methods than recent state-of-the-art generalizable methods.
arXiv Detail & Related papers (2022-04-25T17:14:22Z) - Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose
Estimation [70.32536356351706]
We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations.
We derive suitable measures to quantify prediction uncertainty at both pose and joint level.
We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.
arXiv Detail & Related papers (2022-03-29T07:14:58Z) - PaMIR: Parametric Model-Conditioned Implicit Representation for
Image-based Human Reconstruction [67.08350202974434]
We propose Parametric Model-Conditioned Implicit Representation (PaMIR), which combines the parametric body model with the free-form deep implicit function.
We show that our method achieves state-of-the-art performance for image-based 3D human reconstruction in the cases of challenging poses and clothing types.
arXiv Detail & Related papers (2020-07-08T02:26:19Z) - Monocular Human Pose and Shape Reconstruction using Part Differentiable
Rendering [53.16864661460889]
Recent works succeed in regression-based methods which estimate parametric models directly through a deep neural network supervised by 3D ground truth.
In this paper, we introduce body segmentation as critical supervision.
To improve the reconstruction with part segmentation, we propose a part-level differentiable part that enables part-based models to be supervised by part segmentation.
arXiv Detail & Related papers (2020-03-24T14:25:46Z)
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.