MVDream: Multi-view Diffusion for 3D Generation
We propose MVDream, a multi-view diffusion model that is able to generate
geometrically consistent multi-view images from a given text prompt. By
leveraging image diffusion models pre-trained on large-scale web datasets and a
multi-view dataset rendered from 3D assets, the resulting multi-view diffusion
model can achieve both the generalizability of 2D diffusion and the consistency
of 3D data. Such a model can thus be applied as a multi-view prior for 3D
generation via Score Distillation Sampling, where it greatly improves the
stability of existing 2D-lifting methods by solving the 3D consistency problem.
Finally, we show that the multi-view diffusion model can also be fine-tuned
under a few shot setting for personalized 3D generation, i.e. DreamBooth3D
application, where the consistency can be maintained after learning the subject
identity.
64f19b8099cfca31353b5e06
Yichun Shi
admin_assigned
2023-09-01T10:18:25.618Z
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Shi
user
Yichun
64f19b8099cfca31353b5e07
Peng Wang
claimed_verified
2023-09-01T17:00:38.224Z
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/avatars/e62bc6fed6e9cbe4e0339e050cc3be5f.svg
peng  wang
user
Peng-Wang
64f19b8099cfca31353b5e08
Jianglong Ye
admin_assigned
2023-09-01T10:20:54.620Z
634e2696d00bb5d92c3f4e86
https://cdn-avatars.huggingface.co/v1/production/uploads/1674216878324-634e2696d00bb5d92c3f4e86.jpeg
Jianglong Ye
user
jianglongye
64f19b8099cfca31353b5e09
Mai Long
64f19b8099cfca31353b5e0a
Kejie Li
admin_assigned
2023-09-01T10:22:04.101Z
640f4de8de9b00b4eb9cd698
/avatars/4ecf573eb80f0389cc333ec2b6b26c0e.svg
Kejie Li
user
KejieLi
64f19b8099cfca31353b5e0b
Xiao Yang
claimed_verified
2023-09-04T07:32:20.581Z
62b3d2490385d6048985b42b
/avatars/1c4e534d086d53ef1057fbc00334350a.svg
Xiao Yang
user
seravee008
MVDream: Multi-view Diffusion for 3D Generation
We propose MVDream, a multi-view diffusion model that is able to generate
geometrically consistent multi-view images from a given text prompt. By
leveraging image diffusion models pre-trained on large-scale web datasets and a
multi-view dataset rendered from 3D assets, the resulting multi-view diffusion
model can achieve both the generalizability of 2D diffusion and the consistency
of 3D data. Such a model can thus be applied as a multi-view prior for 3D
generation via Score Distillation Sampling, where it greatly improves the
stability of existing 2D-lifting methods by solving the 3D consistency problem.
Finally, we show that the multi-view diffusion model can also be fine-tuned
under a few shot setting for personalized 3D generation, i.e. DreamBooth3D
application, where the consistency can be maintained after learning the subject
identity.
MVDream generates geometrically consistent multi-view images from text prompts using pre-trained image diffusion models and Score Distillation Sampling, improving 3D generation stability and supporting personalized generation.
multi-view diffusion model text prompt image diffusion models large-scale web datasets multi-view dataset 3D assets Score Distillation Sampling 3D consistency problem fine-tuned few shot setting DreamBooth3D