Snap-Snap: Taking Two Images to Reconstruct 3D Human Gaussians in
  Milliseconds
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/.
68a8214cf83437277232d21c
Jia Lu
68a8214cf83437277232d21d
Taoran Yi
claimed_verified
2025-08-22T08:23:49.974Z
64e5ee4b93d04e3439f4e988
https://cdn-avatars.huggingface.co/v1/production/uploads/64e5ee4b93d04e3439f4e988/g-THJvunoSPUyQ2v_fChU.jpeg
taoranyi
user
thewhole
68a8214cf83437277232d21e
Jiemin Fang
68a8214cf83437277232d21f
Chen Yang
68a8214cf83437277232d220
Chuiyun Wu
68a8214cf83437277232d221
Wei Shen
68a8214cf83437277232d222
Wenyu Liu
68a8214cf83437277232d223
Qi Tian
68a8214cf83437277232d224
Xinggang Wang
Snap-Snap: Taking Two Images to Reconstruct 3D Human Gaussians in
  Milliseconds
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/.
A method reconstructs 3D human bodies from two sparse views using a redesigned geometry model and enhancement algorithm, achieving state-of-the-art performance.
geometry reconstruction model point clouds color information 3D Gaussians rendering quality THuman2.0 cross-domain datasets