Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video
  Diffusion Models
Despite having tremendous progress in image-to-3D generation, existing
methods still struggle to produce multi-view consistent images with
high-resolution textures in detail, especially in the paradigm of 2D diffusion
that lacks 3D awareness. In this work, we present High-resolution Image-to-3D
model (Hi3D), a new video diffusion based paradigm that redefines a single
image to multi-view images as 3D-aware sequential image generation (i.e.,
orbital video generation). This methodology delves into the underlying temporal
consistency knowledge in video diffusion model that generalizes well to
geometry consistency across multiple views in 3D generation. Technically, Hi3D
first empowers the pre-trained video diffusion model with 3D-aware prior
(camera pose condition), yielding multi-view images with low-resolution texture
details. A 3D-aware video-to-video refiner is learnt to further scale up the
multi-view images with high-resolution texture details. Such high-resolution
multi-view images are further augmented with novel views through 3D Gaussian
Splatting, which are finally leveraged to obtain high-fidelity meshes via 3D
reconstruction. Extensive experiments on both novel view synthesis and single
view reconstruction demonstrate that our Hi3D manages to produce superior
multi-view consistency images with highly-detailed textures. Source code and
data are available at https://github.com/yanghb22-fdu/Hi3D-Official.
66e249015293a8105aa793d0
Haibo Yang
66e249015293a8105aa793d1
Yang Chen
66e249015293a8105aa793d2
Yingwei Pan
66e249015293a8105aa793d3
Ting Yao
66e249015293a8105aa793d4
Zhineng Chen
66e249015293a8105aa793d5
Chong-Wah Ngo
66e249015293a8105aa793d6
Tao Mei
admin_assigned
2024-09-12T08:53:45.376Z
66a8d386fbde2b9cb1120393
/avatars/ccfb6e72764e547be2b3528713151693.svg
Tao Mei
user
GiantBision
Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video
  Diffusion Models
Despite having tremendous progress in image-to-3D generation, existing
methods still struggle to produce multi-view consistent images with
high-resolution textures in detail, especially in the paradigm of 2D diffusion
that lacks 3D awareness. In this work, we present High-resolution Image-to-3D
model (Hi3D), a new video diffusion based paradigm that redefines a single
image to multi-view images as 3D-aware sequential image generation (i.e.,
orbital video generation). This methodology delves into the underlying temporal
consistency knowledge in video diffusion model that generalizes well to
geometry consistency across multiple views in 3D generation. Technically, Hi3D
first empowers the pre-trained video diffusion model with 3D-aware prior
(camera pose condition), yielding multi-view images with low-resolution texture
details. A 3D-aware video-to-video refiner is learnt to further scale up the
multi-view images with high-resolution texture details. Such high-resolution
multi-view images are further augmented with novel views through 3D Gaussian
Splatting, which are finally leveraged to obtain high-fidelity meshes via 3D
reconstruction. Extensive experiments on both novel view synthesis and single
view reconstruction demonstrate that our Hi3D manages to produce superior
multi-view consistency images with highly-detailed textures. Source code and
data are available at https://github.com/yanghb22-fdu/Hi3D-Official.
Hi3D is a novel video diffusion model that generates high-resolution, multi-view consistent images with detailed textures using 3D-aware priors and refinement techniques.
video diffusion multi-view images 3D-aware sequential image generation orbital video generation 3D-aware prior camera pose condition 3D-aware video-to-video refiner high-resolution texture details 3D Gaussian Splatting high-fidelity meshes novel view synthesis single view reconstruction