4D-DRESS: A Real-World 4D Dataset of Human Clothing with Detailed Semantic Annotations

Core Concepts

4D-DRESS is the first real-world 4D dataset of human clothing that provides high-quality 4D textured scans, vertex-level semantic labels, and registered garment meshes with SMPL/SMPL-X body models.

Abstract

The 4D-DRESS dataset aims to advance research in human clothing by providing realistic and challenging real-world data. It contains 520 motion sequences capturing 64 human outfits, amounting to a total of 78k frames. Each frame consists of multi-view images, an 80k-face 3D mesh with vertex-level semantic annotations, and a 1k-resolution texture map.
To create this dataset, the authors developed a semi-automatic 4D human parsing pipeline that efficiently combines automation with human-in-the-loop processes to accurately label the complex 4D scans. This pipeline achieves high-quality vertex-level annotations, with only 1.5% of vertices requiring manual rectification.
The dataset offers diverse garment types, including 4 dresses, 30 upper, 28 lower, and 32 outer garments, captured in dynamic motions. The authors quantify the clothing deformations by computing the mean distances from the garments to the registered SMPL body surfaces, which can reach up to 14.76 cm, highlighting the challenging nature of the dataset.
4D-DRESS serves as a valuable resource for various computer vision and graphics tasks, including clothing simulation, reconstruction, and human parsing. The authors establish several benchmarks to evaluate the performance of state-of-the-art methods on these tasks, revealing the limitations of existing approaches in handling the realistic and complex clothing deformations captured in the dataset.

Stats

The mean distance from the garments to the registered SMPL body surfaces can reach up to 14.76 cm, with the 10% most challenging frames exhibiting distances up to 20.09 cm.
The dataset contains a total of 78k frames, capturing 64 distinct real-world human outfits in 520 motion sequences.
Each frame consists of an 80k-face triangle mesh, a 1k resolution textured map, and a set of 1k resolution multi-view images.

Quotes

"4D-DRESS gathers a variety of human subjects and outfits providing accurate semantic labels of human clothing, garment meshes, and SMPL/SMPL-X fits."
"Capturing real-world 4D sequences of humans wearing various clothing and performing diverse motions requires dedicated high-end capture facilities."
"The quality of the ground-truth data in 4D-DRESS allows us to establish several evaluation benchmarks for diverse tasks, including clothing simulation, reconstruction, and human parsing."

Key Insights Distilled From

4D-DRESS: A 4D Dataset of Real-world Human Clothing with Semantic Annotations

by Wenbo Wang,H... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18630.pdf

Deeper Inquiries

How can the semi-automatic 4D human parsing pipeline be further improved to reduce the need for manual rectification and increase the scalability of the dataset creation process

The semi-automatic 4D human parsing pipeline can be enhanced in several ways to reduce the need for manual rectification and improve scalability. One approach is to incorporate advanced machine learning techniques, such as deep neural networks, for more accurate initial labeling of the 4D scans. By training the model on a larger and more diverse dataset, it can learn to recognize and label different types of clothing and body movements more effectively. Additionally, implementing a feedback loop mechanism where the system learns from manual corrections made during the rectification process can help improve the accuracy of future predictions. This continuous learning process can gradually reduce the need for manual intervention over time. Furthermore, optimizing the pipeline for parallel processing and utilizing cloud computing resources can significantly enhance scalability, allowing for faster annotation of a larger volume of data. By leveraging these strategies, the pipeline can become more efficient, accurate, and scalable for creating datasets like 4D-DRESS.

What are the potential applications of the 4D-DRESS dataset beyond the benchmarks presented in the paper, and how can it inspire new research directions in computer vision and graphics

The 4D-DRESS dataset has the potential to be applied in various fields beyond the benchmarks presented in the paper. One key application is in virtual try-on technology, where the dataset can be used to create more realistic and accurate virtual fitting experiences for online shoppers. Retailers can leverage the dataset to develop advanced virtual fitting rooms that simulate how different clothing items drape and move on a person's body in real-time. Additionally, the dataset can be valuable for research in human-computer interaction, animation, and gaming industries. It can inspire new research directions in computer vision and graphics by serving as a benchmark for developing algorithms that can accurately model and simulate real-world clothing dynamics. Researchers can explore novel approaches for clothing reconstruction, motion capture, and human representation learning using the rich and diverse data provided by 4D-DRESS. The dataset can also be utilized for training and testing AI models in areas like pose estimation, semantic segmentation, and 3D reconstruction, contributing to advancements in these fields.

Given the challenges in modeling the realistic clothing deformations captured in 4D-DRESS, what novel approaches or architectures could be developed to better handle such complex real-world data

To address the challenges in modeling realistic clothing deformations captured in 4D-DRESS, novel approaches and architectures can be developed to better handle such complex real-world data. One potential approach is to integrate physics-based simulation techniques with deep learning models to create more accurate and dynamic clothing simulations. By combining the strengths of physics-based simulations in capturing realistic cloth behavior with the flexibility and adaptability of deep learning models, it is possible to achieve more lifelike and detailed clothing deformations. Another strategy is to explore generative adversarial networks (GANs) or variational autoencoders (VAEs) for learning complex clothing dynamics and deformations. These generative models can be trained on the 4D-DRESS dataset to generate realistic clothing variations and movements. Additionally, incorporating attention mechanisms and graph neural networks can help capture long-range dependencies and spatial relationships in clothing deformations, enabling more precise modeling of intricate details like wrinkles, folds, and drapes. By innovating with these advanced techniques and architectures, researchers can overcome the challenges of modeling realistic clothing deformations and enhance the fidelity of clothing simulations and reconstructions.