GECO: Generative Image-to-3D within a SECOnd
3D generation has seen remarkable progress in recent years. Existing
techniques, such as score distillation methods, produce notable results but
require extensive per-scene optimization, impacting time efficiency.
Alternatively, reconstruction-based approaches prioritize efficiency but
compromise quality due to their limited handling of uncertainty. We introduce
GECO, a novel method for high-quality 3D generative modeling that operates
within a second. Our approach addresses the prevalent issues of uncertainty and
inefficiency in current methods through a two-stage approach. In the initial
stage, we train a single-step multi-view generative model with score
distillation. Then, a second-stage distillation is applied to address the
challenge of view inconsistency from the multi-view prediction. This two-stage
process ensures a balanced approach to 3D generation, optimizing both quality
and efficiency. Our comprehensive experiments demonstrate that GECO achieves
high-quality image-to-3D generation with an unprecedented level of efficiency.
6659465c722ca7da96bbf2d4
Chen Wang
admin_assigned
2024-05-31T10:03:16.105Z
62e9d5af6687b60b9c01240a
/avatars/3983be5662aa28aefcb18deaf08d7cb1.svg
Chen Wang
user
chenwang
6659465c722ca7da96bbf2d5
Jiatao Gu
admin_assigned
2024-05-31T10:03:46.273Z
6164e72d73996c363c52e66d
https://cdn-avatars.huggingface.co/v1/production/uploads/1634002684894-noauth.png
Jiatao Gu
user
thomagram
6659465c722ca7da96bbf2d6
Xiaoxiao Long
admin_assigned
2024-05-31T10:03:53.344Z
648ebb653c8611c2ba1dbede
/avatars/dddaf7ae8d76e733b30d9952563ea22b.svg
xiaoxiaolongli
user
xiaoxiaolong
6659465c722ca7da96bbf2d7
Yuan Liu
claimed_verified
2024-06-04T08:35:20.561Z
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/avatars/2c2d22c6b605a9c412a9c37960efeeb2.svg
Yuan Liu
user
liuyuan-pal
6659465c722ca7da96bbf2d8
Lingjie Liu
GECO: Generative Image-to-3D within a SECOnd
3D generation has seen remarkable progress in recent years. Existing
techniques, such as score distillation methods, produce notable results but
require extensive per-scene optimization, impacting time efficiency.
Alternatively, reconstruction-based approaches prioritize efficiency but
compromise quality due to their limited handling of uncertainty. We introduce
GECO, a novel method for high-quality 3D generative modeling that operates
within a second. Our approach addresses the prevalent issues of uncertainty and
inefficiency in current methods through a two-stage approach. In the initial
stage, we train a single-step multi-view generative model with score
distillation. Then, a second-stage distillation is applied to address the
challenge of view inconsistency from the multi-view prediction. This two-stage
process ensures a balanced approach to 3D generation, optimizing both quality
and efficiency. Our comprehensive experiments demonstrate that GECO achieves
high-quality image-to-3D generation with an unprecedented level of efficiency.
GECO, a two-stage generative model using score distillation, achieves efficient high-quality 3D generation from images by addressing view inconsistency.
score distillation multi-view generative model view inconsistency 3D generation image-to-3D generation