ClotheDreamer: Text-Guided Garment Generation with 3D Gaussians
High-fidelity 3D garment synthesis from text is desirable yet challenging for
digital avatar creation. Recent diffusion-based approaches via Score
Distillation Sampling (SDS) have enabled new possibilities but either
intricately couple with human body or struggle to reuse. We introduce
ClotheDreamer, a 3D Gaussian-based method for generating wearable,
production-ready 3D garment assets from text prompts. We propose a novel
representation Disentangled Clothe Gaussian Splatting (DCGS) to enable separate
optimization. DCGS represents clothed avatar as one Gaussian model but freezes
body Gaussian splats. To enhance quality and completeness, we incorporate
bidirectional SDS to supervise clothed avatar and garment RGBD renderings
respectively with pose conditions and propose a new pruning strategy for loose
clothing. Our approach can also support custom clothing templates as input.
Benefiting from our design, the synthetic 3D garment can be easily applied to
virtual try-on and support physically accurate animation. Extensive experiments
showcase our method's superior and competitive performance. Our project page is
at https://ggxxii.github.io/clothedreamer.
667a5e85becec8fc5106baae
Yufei Liu
admin_assigned
2024-06-25T13:41:03.089Z
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Yufei Liu
user
ggxxii
667a5e85becec8fc5106baaf
Junshu Tang
admin_assigned
2024-06-25T13:41:09.739Z
634e67f5d049354d7ee0caa4
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junshu tang
user
tangjs
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Chu Zheng
admin_assigned
2024-06-25T13:41:15.302Z
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/avatars/7c51bd7c5bcd32ba4cfaf9cbe502cef0.svg
chuzheng
user
chuzheng
667a5e85becec8fc5106bab1
Shijie Zhang
667a5e85becec8fc5106bab2
Jinkun Hao
667a5e85becec8fc5106bab3
Junwei Zhu
admin_assigned
2024-06-25T13:41:52.718Z
65aadb2b9aba49e1d03cfccc
/avatars/b7c48dc999bf2f94018315b546d50eeb.svg
Junwei Zhu
user
ktpt123
667a5e85becec8fc5106bab4
Dongjin Huang
ClotheDreamer: Text-Guided Garment Generation with 3D Gaussians
High-fidelity 3D garment synthesis from text is desirable yet challenging for
digital avatar creation. Recent diffusion-based approaches via Score
Distillation Sampling (SDS) have enabled new possibilities but either
intricately couple with human body or struggle to reuse. We introduce
ClotheDreamer, a 3D Gaussian-based method for generating wearable,
production-ready 3D garment assets from text prompts. We propose a novel
representation Disentangled Clothe Gaussian Splatting (DCGS) to enable separate
optimization. DCGS represents clothed avatar as one Gaussian model but freezes
body Gaussian splats. To enhance quality and completeness, we incorporate
bidirectional SDS to supervise clothed avatar and garment RGBD renderings
respectively with pose conditions and propose a new pruning strategy for loose
clothing. Our approach can also support custom clothing templates as input.
Benefiting from our design, the synthetic 3D garment can be easily applied to
virtual try-on and support physically accurate animation. Extensive experiments
showcase our method's superior and competitive performance. Our project page is
at https://ggxxii.github.io/clothedreamer.
ClotheDreamer generates 3D garment assets from text using a Gaussian-based method with disentangled representation and bidirectional Score Distillation Sampling, enabling high-quality, reusable garments suitable for virtual try-on and animation.
3D garment synthesis text prompts Gaussian-based method Disentangled Clothe Gaussian Splatting DCDS bidirectional Score Distillation Sampling SDS Gaussian splats RGBD renderings pruning strategy virtual try-on physically accurate animation