HumanSplat: Generalizable Single-Image Human Gaussian Splatting with
  Structure Priors
Despite recent advancements in high-fidelity human reconstruction techniques,
the requirements for densely captured images or time-consuming per-instance
optimization significantly hinder their applications in broader scenarios. To
tackle these issues, we present HumanSplat which predicts the 3D Gaussian
Splatting properties of any human from a single input image in a generalizable
manner. In particular, HumanSplat comprises a 2D multi-view diffusion model and
a latent reconstruction transformer with human structure priors that adeptly
integrate geometric priors and semantic features within a unified framework. A
hierarchical loss that incorporates human semantic information is further
designed to achieve high-fidelity texture modeling and better constrain the
estimated multiple views. Comprehensive experiments on standard benchmarks and
in-the-wild images demonstrate that HumanSplat surpasses existing
state-of-the-art methods in achieving photorealistic novel-view synthesis.
66729ae8cbf550c42a1401bb
Panwang Pan
claimed_verified
2024-06-19T20:19:18.500Z
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Panwang Pan
user
paulpanwang
66729ae8cbf550c42a1401bc
Zhuo Su
66729ae8cbf550c42a1401bd
Chenguo Lin
admin_assigned
2024-06-19T21:08:02.942Z
62e18206926f4892a4c782bd
https://cdn-avatars.huggingface.co/v1/production/uploads/62e18206926f4892a4c782bd/m_OAeRb2a7a5PlnK6d4Dg.jpeg
Chenguo Lin
user
chenguolin
66729ae8cbf550c42a1401be
Zhen Fan
66729ae8cbf550c42a1401bf
Yongjie Zhang
admin_assigned
2024-06-19T21:08:14.272Z
64c62590ed521f27a41f9338
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Zhang Yongjie
user
yongjie-zhang-mail
66729ae8cbf550c42a1401c0
Zeming Li
admin_assigned
2024-06-19T21:08:21.013Z
65c04adbc63d6a8d7f217c4d
/avatars/3407dd73d1f5cf41c56cee7542858f93.svg
Zeming Li
user
ZemingLi
66729ae8cbf550c42a1401c1
Tingting Shen
66729ae8cbf550c42a1401c2
Yadong Mu
66729ae8cbf550c42a1401c3
Yebin Liu
admin_assigned
2024-06-19T21:07:43.094Z
62e14dbe4db2175cd2735a80
/avatars/e6385dcedcb97e1b36281b49210321aa.svg
Yebin Liu
user
YebinLiu
HumanSplat: Generalizable Single-Image Human Gaussian Splatting with
  Structure Priors
Despite recent advancements in high-fidelity human reconstruction techniques,
the requirements for densely captured images or time-consuming per-instance
optimization significantly hinder their applications in broader scenarios. To
tackle these issues, we present HumanSplat which predicts the 3D Gaussian
Splatting properties of any human from a single input image in a generalizable
manner. In particular, HumanSplat comprises a 2D multi-view diffusion model and
a latent reconstruction transformer with human structure priors that adeptly
integrate geometric priors and semantic features within a unified framework. A
hierarchical loss that incorporates human semantic information is further
designed to achieve high-fidelity texture modeling and better constrain the
estimated multiple views. Comprehensive experiments on standard benchmarks and
in-the-wild images demonstrate that HumanSplat surpasses existing
state-of-the-art methods in achieving photorealistic novel-view synthesis.
HumanSplat predicts 3D Gaussian Splatting properties using a 2D multi-view diffusion model and latent reconstruction transformer, achieving photorealistic novel-view synthesis.
3D Gaussian Splatting 2D multi-view diffusion model latent reconstruction transformer human structure priors hierarchical loss photorealistic novel-view synthesis