Snap-Snap: Taking Two Images to Reconstruct 3D Human Gaussians in
  Milliseconds
Authors: name: Jia Lu, name: Taoran Yi, name: Jiemin Fang, name: Chen Yang, name: Chuiyun Wu, name: Wei Shen, name: Wenyu Liu, name: Qi Tian, name: Xinggang Wang

Reconstructing 3D human bodies from sparse views has been an appealing topic,
which is crucial to broader the related applications. In this paper, we propose
a quite challenging but valuable task to reconstruct the human body from only
two images, i.e., the front and back view, which can largely lower the barrier
for users to create their own 3D digital humans. The main challenges lie in the
difficulty of building 3D consistency and recovering missing information from
the highly sparse input. We redesign a geometry reconstruction model based on
foundation reconstruction models to predict consistent point clouds even input
images have scarce overlaps with extensive human data training. Furthermore, an
enhancement algorithm is applied to supplement the missing color information,
and then the complete human point clouds with colors can be obtained, which are
directly transformed into 3D Gaussians for better rendering quality.
Experiments show that our method can reconstruct the entire human in 190 ms on
a single NVIDIA RTX 4090, with two images at a resolution of 1024x1024,
demonstrating state-of-the-art performance on the THuman2.0 and cross-domain
datasets. Additionally, our method can complete human reconstruction even with
images captured by low-cost mobile devices, reducing the requirements for data
collection. Demos and code are available at
https://hustvl.github.io/Snap-Snap/.