单图秒级生成高保真3D视角，支持真实尺度相机运动。
Sharp Monocular View Synthesis in Less Than a Second
- 用神经网络直接回归3D高斯表示参数，单次前向传播完成重建。
- 合成时间降低1000倍，LPIPS下降25%-34%，DISTS下降21%-43%。
- 无需微调即可跨数据集泛化，适合实时3D视觉应用开发。
我们提出SHARP，一种从单张图像实现高保真视角合成的方法。给定一张照片，SHARP通过一个神经网络在标准GPU上仅需不到一秒，即可回归出场景的3D高斯表示参数。该表示支持真实尺度的相机运动，可实时渲染出高分辨率的逼真图像。实验表明，SHARP在多个数据集上实现了稳健的零样本泛化能力，相较最佳先前模型，将LPIPS降低25%-34%，DISTS降低21%-43%，同时合成时间缩短三个数量级。代码与权重已开源：https://github.com/apple/ml-sharp。
原文摘要 · Abstract (English)
We present SHARP, an approach to photorealistic view synthesis from a single image. Given a single photograph, SHARP regresses the parameters of a 3D Gaussian representation of the depicted scene. This is done in less than a second on a standard GPU via a single feedforward pass through a neural network. The 3D Gaussian representation produced by SHARP can then be rendered in real time, yielding high-resolution photorealistic images for nearby views. The representation is metric, with absolute scale, supporting metric camera movements. Experimental results demonstrate that SHARP delivers robust zero-shot generalization across datasets. It sets a new state of the art on multiple datasets, reducing LPIPS by 25-34% and DISTS by 21-43% versus the best prior model, while lowering the synthesis time by three orders of magnitude. Code and weights are provided at https://github.com/apple/ml-sharp
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