仅用一张图快速生成高保真3D物体，70秒完成
Single Image to High-Quality 3D Object via Latent Features
- 基于预训练潜空间编码器，从图像潜特征逐步生成3D几何与纹理
- 生成的3D模型高度还原输入图像，全流程仅需70秒
- 小样本训练即达先进水平，适合快速建模需求
3D资产在数字时代至关重要。尽管近年来图像到3D的自动生成取得显著进展，但仍难以同时实现快速、精细和高保真生成。本文提出LatentDreamer，一种从单张图像生成3D物体的新框架。其核心是使用预训练的变分自编码器将3D几何映射到潜空间特征，大幅降低生成难度。该方法从潜特征出发，分步生成粗略几何、精细几何和真实纹理。生成的3D物体与输入图像高度一致，整个过程通常在70秒内完成。大量实验表明，仅需少量训练，该方法性能已达到当前主流水平。
原文摘要 · Abstract (English)
3D assets are essential in the digital age. While automatic 3D generation, such as image-to-3d, has made significant strides in recent years, it often struggles to achieve fast, detailed, and high-fidelity generation simultaneously. In this work, we introduce LatentDreamer, a novel framework for generating 3D objects from single images. The key to our approach is a pre-trained variational autoencoder that maps 3D geometries to latent features, which greatly reducing the difficulty of 3D generation. Starting from latent features, the pipeline of LatentDreamer generates coarse geometries, refined geometries, and realistic textures sequentially. The 3D objects generated by LatentDreamer exhibit high fidelity to the input images, and the entire generation process can be completed within a short time (typically in 70 seconds). Extensive experiments show that with only a small amount of training, LatentDreamer demonstrates competitive performance compared to contemporary approachs.
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