ShapeBoost: Boosting Human Shape Estimation with Part-Based Parameterization and Clothing-Preserving Augmentation
(2403.01345)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.
Overview
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ShapeBoost is introduced as a new framework that significantly improves human shape recovery from images by utilizing part-based shape parameterization and a novel data augmentation technique.
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It offers a more descriptive and locally relevant representation of human shapes, aiding in overcoming challenges posed by PCA-based methods.
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The technique for clothing-preserving data augmentation tackles the issue of data diversity, especially for extreme body types, without compromising clothing details.
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Experimental results show ShapeBoost outperforming state-of-the-art methods in accuracy for diverse body shapes and clothing, suggesting great potential for virtual and augmented reality applications.
Advancing Human Shape Estimation through Part-based Parameterization and Data Augmentation
Overview
The field of computer vision has long sought to accurately recover human shapes from monocular RGB images. This task, while crucial for various applications such as virtual reality and augmented reality, presents significant challenges due to the diversity of human body shapes and the complexity introduced by clothing. This work introduces ShapeBoost, a novel framework that significantly enhances human shape recovery by deploying a unique part-based shape parameterization and a cutting-edge clothing-preserving data augmentation technique.
Part-based Shape Parameterization
Traditional methods often depend on PCA-based shape coefficients to describe human shapes, a technique that provides a global descriptor but lacks local specificity and interpretability. ShapeBoost departs from this approach by proposing a new way to parameterize human shapes. The novel parameterization segments the human body into distinct parts, describing each segment with bone lengths and the mean width of part slices. This method yields a more descriptive and locally relevant representation of human shapes, which is particularly advantageous for learning from local image features and mitigating overfitting issues.
Clothing-preserving Data Augmentation
One of the perpetual challenges in human shape estimation is the lack of image data featuring diverse body shapes, especially for individuals with extreme body types. ShapeBoost addresses this limitation with a clothing-preserving data augmentation module that generates realistic images of various human shapes without altering clothing details. The process involves segmenting the human body from an image, applying a transformation to change its shape, and then pasting it back onto the original background. This approach ensures the generation of accurate, diverse body shape data, improving the model's performance in estimating shapes under different clothing types.
Experimental Results
ShapeBoost's effectiveness is underscored by its performance on benchmark datasets, where it outperforms other state-of-the-art methods in estimating human shapes for people with extreme body types and those wearing various types of clothing. The framework demonstrates superior accuracy in pixel-level alignment for diverse body shapes, validating the potential of part-based parameterization and the proposed data augmentation technique.
Implications and Future Directions
The introduction of ShapeBoost heralds a significant step forward in the realm of human shape estimation. The framework's ability to accurately estimate human shapes under challenging conditions—such as varied clothing and extreme body shapes—could have profound implications for the development of more immersive and interactive virtual and augmented reality experiences.
Moreover, the novelty of the part-based shape parameterization opens new avenues for research, suggesting that further exploration and refinement of this approach could lead to even more accurate human shape recovery techniques. The success of the clothing-preserving data augmentation module also indicates potential applications beyond human shape estimation, including generating synthetic datasets for training other computer vision models.
Given the promising results demonstrated by ShapeBoost, future work may explore extending the framework's capabilities to more complex human poses and interactions, improving the efficiency and robustness of the shape reconstruction algorithm, and further refining the data augmentation techniques to cover a broader spectrum of human shapes and clothing types.
Acknowledgments
The recognition of the supporting entities behind ShapeBoost, including research institutes and funding bodies, underscores the collaborative effort required to push the boundaries of what's achievable in human shape estimation and the broader field of computer vision.
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