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

Kernkonzepte

ShapeBoost introduces a novel part-based parameterization for accurate human shape recovery, outperforming existing methods by achieving high accuracy in diverse body shapes and clothing situations.

Zusammenfassung

ShapeBoost proposes a new approach to human shape recovery, utilizing part-based parameterization and clothing-preserving augmentation. The method surpasses state-of-the-art techniques in accurately estimating body shapes under various conditions.

Accurate human shape estimation from monocular RGB images is challenging due to diverse body shapes and clothing variations. ShapeBoost introduces a novel framework that decomposes human shape into bone lengths and part widths, achieving pixel-level alignment even for rare body shapes and high accuracy for people wearing different types of clothes. By adopting a new parameterization technique, the method balances flexibility and validity using a semi-analytical shape reconstruction algorithm. The proposed clothing-preserving data augmentation module generates realistic images with diverse body shapes and accurate annotations. Experimental results demonstrate the superiority of ShapeBoost over existing methods in handling diverse body shapes and varied clothing situations.

Previous approaches have struggled with overfitting on body shape estimation due to limited datasets featuring diverse body shapes. ShapeBoost addresses this limitation by proposing a new parameterization that decomposes human shape into bone lengths and part widths, enabling accurate reconstruction of extreme body shapes while encouraging pixel-level alignment. The method outperforms state-of-the-art techniques in both thick clothes situations and extreme body shape scenarios.

The main contributions of ShapeBoost include an accurate and robust human shape parameterization, a clothing-preserving data augmentation module, and a shape reconstruction framework that outperforms previous approaches in handling diverse clothing as well as extreme body shapes.

Statistiken

Vertex error (mm): 100, 50

Zitate

Wichtige Erkenntnisse aus

ShapeBoost

by Siyuan Bian,... um arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01345.pdf

Tiefere Fragen

How does the use of part-based parameterization improve the accuracy of human shape recovery

The use of part-based parameterization improves the accuracy of human shape recovery by providing a more flexible and interpretable representation of the human body. Unlike traditional methods that rely on global descriptors like PCA coefficients, the part-based parameterization decomposes the human shape into bone lengths and mean widths of each part slice. This approach allows for a more localized understanding of the body shape, making it easier to regress and more adaptable to different body shapes and clothing types. By focusing on local image features, the model can better capture fine details and variations in body shapes, leading to higher accuracy in both diverse body shapes and varied clothing situations.

What are the implications of not considering pose-dependent shape deformation in the prediction process

Not considering pose-dependent shape deformation in the prediction process can have significant implications on the accuracy of human shape estimation. When predicting bone lengths and part widths based on rest-pose SMPL models without accounting for pose-dependent deformations, there may be discrepancies between predicted shapes and actual poses. Pose-dependent deformations play a crucial role in determining how different parts of the body interact with each other during movement or specific poses. Ignoring these deformations can lead to inaccuracies in predicting realistic body shapes under various poses, potentially resulting in misalignments or distortions in reconstructed meshes.

How can the concept of clothing-preserving augmentation be applied to other computer vision tasks beyond human shape estimation

The concept of clothing-preserving augmentation can be applied to other computer vision tasks beyond human shape estimation where preserving contextual information is essential for accurate predictions. For example:

Object Detection: In scenarios where objects are partially occluded or affected by environmental factors (e.g., lighting conditions), preserving context through augmentation techniques could improve object detection performance.
Semantic Segmentation: Augmenting images while maintaining semantic boundaries could enhance segmentation accuracy by ensuring consistency across transformed images.
Image Translation: When translating images from one domain to another (e.g., style transfer), preserving key elements such as textures or structures through augmentation could result in more realistic translations.
By incorporating clothing-preserving strategies into these tasks, models can learn robust representations that generalize well across different contexts while retaining important visual cues necessary for accurate predictions.