High-fidelity 3D Human Digitization from Single 2K Resolution Images (2303.15108v1)

Abstract: High-quality 3D human body reconstruction requires high-fidelity and large-scale training data and appropriate network design that effectively exploits the high-resolution input images. To tackle these problems, we propose a simple yet effective 3D human digitization method called 2K2K, which constructs a large-scale 2K human dataset and infers 3D human models from 2K resolution images. The proposed method separately recovers the global shape of a human and its details. The low-resolution depth network predicts the global structure from a low-resolution image, and the part-wise image-to-normal network predicts the details of the 3D human body structure. The high-resolution depth network merges the global 3D shape and the detailed structures to infer the high-resolution front and back side depth maps. Finally, an off-the-shelf mesh generator reconstructs the full 3D human model, which are available at https://github.com/SangHunHan92/2K2K. In addition, we also provide 2,050 3D human models, including texture maps, 3D joints, and SMPL parameters for research purposes. In experiments, we demonstrate competitive performance over the recent works on various datasets.

• The paper introduces the 2K2K method, enabling high-fidelity 3D human reconstruction from single 2K images via part-wise segmentation.
• It leverages a comprehensive dataset of over 2,000 3D human models captured with 80 DSLR cameras to enhance training and precision.
• Quantitative results show improved detail and efficiency, offering significant advancements for VR, gaming, and content creation applications.

High-Fidelity 3D Human Digitization from Single 2K Resolution Images

The paper "High-fidelity 3D Human Digitization from Single 2K Resolution Images" presents an innovative approach to reconstruct high-fidelity 3D human models from single high-resolution images using a method named 2K2K. Given the increasing demand for realistic 3D human models in various applications such as gaming, virtual reality, and online content creation, the authors propose a novel framework that effectively exploits 2K resolution inputs to address the quality and detail inadequacies prevalent in existing solutions.

The core contribution of the research is the creation of the 2K2K method, which builds on part-wise segmentation to handle large image resolutions efficiently. The paper identifies two common approaches to single-image 3D human reconstruction: the prediction of deep implicit volumes and the derivation of multi-view depth maps. Both these methods encounter scalability issues with high-resolution images, which this work aims to circumvent by segmenting the human body into multiple parts—arms, legs, torso, etc.—and performing separate image-to-normal predictions for each. This localized approach reduces computational load while improving the precision of surface normal predictions, which is crucial for producing high-resolution depth maps and, subsequently, detailed 3D models.

A critical underpinning of the proposed framework is a new, extensive dataset composed of over 2,000 3D human models that were captured using a sophisticated setup incorporating 80 DSLR cameras, ensuring high geometric fidelity. The dataset not only aids model training but also provides a rich source of high-fidelity data for broader research applications, addressing extant public databases' limitations regarding detail and volume.

Quantitative assessments show the proposed method achieves competitive performance relative to existing benchmarks and noticeable improvements in inference time, demonstrating that the method filled a gap in the efficiency and quality of existing techniques. In particular, the method demonstrates superior detailing capabilities in capturing human models' nuanced structures, such as facial features, through high-resolution input images, audited through standard metrics like P2S distance and surface normal error.

The practical implications of this work are substantial, highlighting its potential to enhance virtual realities' experiential realism and empower individualized content production, benefitting sectors from fashion to entertainment. Additionally, the introduction of the dataset and an original approach to high-resolution data handling will stimulate further advancements in computer vision and 3D reconstruction technologies by providing a robust foundational toolset.

Looking towards the future, the architectural innovations presented may inspire more generalized methodologies applicable to other forms of high-detail 3D reconstructions from limited image sources. Furthermore, possible transliterations of these methods into real-time applications could catalyze the evolution of immersive experiences in augmented reality platforms and beyond.

In summary, "High-fidelity 3D Human Digitization from Single 2K Resolution Images" contributes significantly to the domain by matching the growing ambition of technology with robust scientific methodologies, presenting both a technical advancement and a valuable resource for the research community.