仅用一张图5分钟生成高质量3D模型，还原可见与隐藏部分。
Vista3D: Unravel the 3D Darkside of a Single Image
- 分两阶段生成：先快速建模，再优化几何细节。
- 用双隐式函数分离可见与遮挡部分，提升重建精度。
- 适合需要快速高保真3D生成的研究者与开发者。
我们致力于从单一图像中揭示物体的隐藏维度。为此，提出Vista3D框架，可在5分钟内实现快速且一致的3D生成。其核心为两阶段方法：粗略阶段通过单图快速生成初始几何结构，采用高斯溅射（Gaussian Splatting）；精细阶段则直接从学习到的高斯溅射中提取符号距离函数（SDF），并利用可微分等值面表示进行优化。此外，通过两个独立的隐式函数构建解耦表征，分别捕捉物体的可见与被遮挡部分，显著提升生成质量。同时，通过角向扩散先验组合，融合2D扩散先验与3D感知扩散先验，实现梯度协调。大量实验表明，Vista3D有效平衡了生成结果的一致性与多样性。演示与代码将开源于https://github.com/florinshen/Vista3D。
原文摘要 · Abstract (English)
We embark on the age-old quest: unveiling the hidden dimensions of objects from mere glimpses of their visible parts. To address this, we present Vista3D, a framework that realizes swift and consistent 3D generation within a mere 5 minutes. At the heart of Vista3D lies a two-phase approach: the coarse phase and the fine phase. In the coarse phase, we rapidly generate initial geometry with Gaussian Splatting from a single image. In the fine phase, we extract a Signed Distance Function (SDF) directly from learned Gaussian Splatting, optimizing it with a differentiable isosurface representation. Furthermore, it elevates the quality of generation by using a disentangled representation with two independent implicit functions to capture both visible and obscured aspects of objects. Additionally, it harmonizes gradients from 2D diffusion prior with 3D-aware diffusion priors by angular diffusion prior composition. Through extensive evaluation, we demonstrate that Vista3D effectively sustains a balance between the consistency and diversity of the generated 3D objects. Demos and code will be available at https://github.com/florinshen/Vista3D.
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