基于图片的人物类别3D打印模型生成数据

通过构建一个包含大量不同类别人物（如士兵、宇航员、不同职业和姿态的角色）、且均为水密性的3D打印模型及其对应的单张或多张渲染图片的大规模配对数据集，可以为深度学习模型提供训练基础，使其学习从二维人物图像生成完整的三维几何。这一数据集主要适用于桌面游戏棋子和微缩模型的定制、个性化人形玩偶（手办）的快速制作、虚拟化身的实体化打印以及数字人内容创作等领域。利用该数据训练出的模型，能够让用户通过上传一张人物照片来生成一个可直接用于3D打印的对应模型，解决了从零开始制作人物模型技术难度极高、周期漫长且成本昂贵的问题。基于单张图片生成特定类别（如人物）的可3D打印模型，旨在让三维人形创作大众化。具体过程包括：（1）数据收集：用户提供一张包含清晰人物主体的RGB图片（I_rgb），例如一张全身或半身的人物照片。（2）数据处理：将输入的图片送入一个在人物图像上经过优化的预训练图像编码器，以提取捕捉人物姿态、服装和身体轮廓的深度特征向量。特征向量通过公式 F_image = Encoder_image(I_rgb) 提取，其中 F_image 为图像特征向量，Encoder_image 为图像编码器。（3）模型构建：使用提取的图像特征向量作为输入，设计并搭建一个专注于人体几何生成的深度解码模型。该模型从特征中推断并生成人物的隐式三维表示。根据公式 SDF = Decoder_3D(F_image) 从图像特征中解码出三维人物模型，Decoder_3D 为三维形状解码器。关键的评估指标包括交并比（Intersection over Union, IoU）和倒角距离（Chamfer Distance, CD）。此方法适用于从单张照片快速生成个性化的人物模型，极大地降低了人物角色的3D打印门槛。

By constructing a large-scale paired dataset containing numerous human figures of various categories (e.g., soldiers, astronauts, characters with different occupations and poses) as well as their corresponding single or multiple rendered images, all of which are water-tight 3D printable models, this dataset can provide training foundations for deep learning models to learn to generate complete 3D geometry from 2D human images. This dataset is mainly applicable to scenarios such as customization of tabletop game pieces and miniatures, rapid production of customized humanoid figurines, physical printing of virtual avatars, and digital human content creation. Models trained on this dataset allow users to upload a single portrait of a person to generate a corresponding model that can be directly used for 3D printing, solving the problems of extremely high technical difficulty, long production cycles and high costs associated with creating human models from scratch. Generating 3D-printable models of specific categories (e.g., human figures) from a single image aims to democratize 3D humanoid creation. The specific process includes: (1) Data Collection: Users provide an RGB image ($I_{ ext{rgb}}$) containing a clearly focused human subject, such as a full-body or half-body portrait of a person. (2) Data Processing: The input image is fed into a pre-trained image encoder optimized on human images to extract deep feature vectors that capture human poses, clothing and body contours. The feature vector is extracted via the formula $F_{ ext{image}} = ext{Encoder}_{ ext{image}}(I_{ ext{rgb}})$, where $F_{ ext{image}}$ denotes the image feature vector and $ ext{Encoder}_{ ext{image}}$ is the image encoder. (3) Model Construction: Using the extracted image feature vector as input, a deep decoding model focused on human geometry generation is designed and built. This model infers and generates an implicit 3D representation of the human subject from the features. The 3D human model is decoded from the image features via the formula $ ext{SDF} = ext{Decoder}_{ ext{3D}}(F_{ ext{image}})$, where $ ext{Decoder}_{ ext{3D}}$ is the 3D shape decoder. Key evaluation metrics include Intersection over Union (IoU) and Chamfer Distance (CD). This method enables rapid generation of personalized human models from a single photograph, greatly lowering the barrier to 3D printing for human characters.

搜集汇总

背景与挑战

背景概述

该数据集包含3227条CSV格式数据，用于从单张RGB图片生成人物类别的3D打印模型，包括图像特征向量和三维模型等字段，并采用IoU和CD指标评估模型质量。它支持桌面游戏棋子、个性化玩偶等应用，通过深度学习算法实现从二维图像到三维几何的转换，降低了3D打印人物模型的技术门槛和成本。