HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a
  Single Image
3D content creation from a single image is a long-standing yet highly
desirable task. Recent advances introduce 2D diffusion priors, yielding
reasonable results. However, existing methods are not hyper-realistic enough
for post-generation usage, as users cannot view, render and edit the resulting
3D content from a full range. To address these challenges, we introduce
HyperDreamer with several key designs and appealing properties: 1) Viewable:
360 degree mesh modeling with high-resolution textures enables the creation of
visually compelling 3D models from a full range of observation points. 2)
Renderable: Fine-grained semantic segmentation and data-driven priors are
incorporated as guidance to learn reasonable albedo, roughness, and specular
properties of the materials, enabling semantic-aware arbitrary material
estimation. 3) Editable: For a generated model or their own data, users can
interactively select any region via a few clicks and efficiently edit the
texture with text-based guidance. Extensive experiments demonstrate the
effectiveness of HyperDreamer in modeling region-aware materials with
high-resolution textures and enabling user-friendly editing. We believe that
HyperDreamer holds promise for advancing 3D content creation and finding
applications in various domains.
657295b5b268b1c509537ee2
Tong Wu
claimed_verified
2024-12-11T09:25:02.147Z
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Tong Wu
user
wutong16
657295b5b268b1c509537ee3
Zhibing Li
admin_assigned
2023-12-08T10:26:02.113Z
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Zhibing LI
user
lizb6626
657295b5b268b1c509537ee4
Shuai Yang
admin_assigned
2023-12-08T10:25:55.489Z
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Shuai Yang
user
PKUWilliamYang
657295b5b268b1c509537ee5
Pan Zhang
admin_assigned
2023-12-08T10:25:17.080Z
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Pan Zhang
user
Gatorburger
657295b5b268b1c509537ee6
Xinggang Pan
657295b5b268b1c509537ee7
Jiaqi Wang
claimed_verified
2024-06-09T20:07:23.291Z
64b4eec4faa3181a5eab9c46
/avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg
Jiaqi Wang
user
myownskyW7
657295b5b268b1c509537ee8
Dahua Lin
admin_assigned
2023-12-08T10:24:12.752Z
636317ed80c1a705a6eff396
/avatars/3db090e101b916d9256d0d3e043db71d.svg
Dahua Lin
user
lindahua
657295b5b268b1c509537ee9
Ziwei Liu
admin_assigned
2023-12-08T10:24:05.871Z
62ab1ac1d48b4d8b048a3473
https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a
  Single Image
3D content creation from a single image is a long-standing yet highly
desirable task. Recent advances introduce 2D diffusion priors, yielding
reasonable results. However, existing methods are not hyper-realistic enough
for post-generation usage, as users cannot view, render and edit the resulting
3D content from a full range. To address these challenges, we introduce
HyperDreamer with several key designs and appealing properties: 1) Viewable:
360 degree mesh modeling with high-resolution textures enables the creation of
visually compelling 3D models from a full range of observation points. 2)
Renderable: Fine-grained semantic segmentation and data-driven priors are
incorporated as guidance to learn reasonable albedo, roughness, and specular
properties of the materials, enabling semantic-aware arbitrary material
estimation. 3) Editable: For a generated model or their own data, users can
interactively select any region via a few clicks and efficiently edit the
texture with text-based guidance. Extensive experiments demonstrate the
effectiveness of HyperDreamer in modeling region-aware materials with
high-resolution textures and enabling user-friendly editing. We believe that
HyperDreamer holds promise for advancing 3D content creation and finding
applications in various domains.
HyperDreamer enables realistic and editable 3D modeling from a single image using viewable 360-degree meshes, renderable materials, and text-guided editing.
2D diffusion priors 360 degree mesh modeling high-resolution textures semantic segmentation data-driven priors albedo roughness specular properties semantic-aware material estimation region-aware materials text-based guidance