ShapeBoost: Boosting Human Shape Estimation with Part-Based Parameterization and Clothing-Preserving Augmentation

• URL: http://arxiv.org/abs/2403.01345v1
• Date: Sat, 2 Mar 2024 23:40:23 GMT
• Title: ShapeBoost: Boosting Human Shape Estimation with Part-Based Parameterization and Clothing-Preserving Augmentation
• Authors: Siyuan Bian, Jiefeng Li, Jiasheng Tang, Cewu Lu
• Abstract summary: We propose ShapeBoost, a new human shape recovery framework. It achieves pixel-level alignment even for rare body shapes and high accuracy for people wearing different types of clothes.
• Score: 58.50613393500561
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate human shape recovery from a monocular RGB image is a challenging task because humans come in different shapes and sizes and wear different clothes. In this paper, we propose ShapeBoost, a new human shape recovery framework that achieves pixel-level alignment even for rare body shapes and high accuracy for people wearing different types of clothes. Unlike previous approaches that rely on the use of PCA-based shape coefficients, we adopt a new human shape parameterization that decomposes the human shape into bone lengths and the mean width of each part slice. This part-based parameterization technique achieves a balance between flexibility and validity using a semi-analytical shape reconstruction algorithm. Based on this new parameterization, a clothing-preserving data augmentation module is proposed to generate realistic images with diverse body shapes and accurate annotations. Experimental results show that our method outperforms other state-of-the-art methods in diverse body shape situations as well as in varied clothing situations.

Related papers

• Neural-ABC: Neural Parametric Models for Articulated Body with Clothes [29.04941764336255]
  We introduce Neural-ABC, a novel model that can represent clothed human bodies with disentangled latent spaces for identity, clothing, shape, and pose. Our model excels at disentangling clothing and identity in different shape and poses while preserving the style of the clothing. Compared to other state-of-the-art parametric models, Neural-ABC demonstrates powerful advantages in the reconstruction of clothed human bodies.
  arXiv  Detail & Related papers  (2024-04-06T16:29:10Z)
• TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model [4.474107938692397]
  Human shape spaces have been extensively studied, as they are a core element of human shape and pose inference tasks. We create an anatomical template, consisting of skeleton bones and soft tissue, to the surface scans of the CAESAR database. This data is then used to learn an anthropo constrained volumetric human shape model in a self-supervised fashion.
  arXiv  Detail & Related papers  (2023-11-03T07:42:19Z)
• 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)
• Detailed Avatar Recovery from Single Image [50.82102098057822]
  This paper presents a novel framework to recover emphdetailed avatar from a single image. We use the deep neural networks to refine the 3D shape in a Hierarchical Mesh Deformation framework. Our method can restore detailed human body shapes with complete textures beyond skinned models.
  arXiv  Detail & Related papers  (2021-08-06T03:51:26Z)
• Deep Physics-aware Inference of Cloth Deformation for Monocular Human Performance Capture [84.73946704272113]
  We show how integrating physics into the training process improves the learned cloth deformations and allows modeling clothing as a separate piece of geometry. Our approach leads to a significant improvement over current state-of-the-art methods and is thus a clear step towards realistic monocular capture of the entire deforming surface of a human clothed.
  arXiv  Detail & Related papers  (2020-11-25T16:46:00Z)
• NiLBS: Neural Inverse Linear Blend Skinning [59.22647012489496]
  We introduce a method to invert the deformations undergone via traditional skinning techniques via a neural network parameterized by pose. The ability to invert these deformations allows values (e.g., distance function, signed distance function, occupancy) to be pre-computed at rest pose, and then efficiently queried when the character is deformed.
  arXiv  Detail & Related papers  (2020-04-06T20:46:37Z)
• 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)
• RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation [17.0630180888369]
  We propose a novel efficient and lightweight model for human pose estimation from a single image. Our model is designed to achieve competitive results at a fraction of the number of parameters and computational cost of various state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-10T16:44:45Z)

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.