title: ClotheDreamer: Text-Guided Garment Generation with 3D Gaussians
summary: 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.
paper_authors._id: 667a5e85becec8fc5106baae
paper_authors.name: Yufei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-25T13:41:03.089Z
paper_authors._id: 645dd4a058f9ee3151493022
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paper_authors.fullname: Yufei Liu
paper_authors.type: user
paper_authors.user: ggxxii
paper_authors._id: 667a5e85becec8fc5106baaf
paper_authors.name: Junshu Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-25T13:41:09.739Z
paper_authors._id: 634e67f5d049354d7ee0caa4
paper_authors.avatarUrl: /avatars/13f7a6f66060590e0394eb888148716c.svg
paper_authors.fullname: junshu tang
paper_authors.type: user
paper_authors.user: tangjs
paper_authors._id: 667a5e85becec8fc5106bab0
paper_authors.name: Chu Zheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-25T13:41:15.302Z
paper_authors._id: 63bd0bf2d8d676a229ab1f25
paper_authors.avatarUrl: /avatars/7c51bd7c5bcd32ba4cfaf9cbe502cef0.svg
paper_authors.fullname: chuzheng
paper_authors.type: user
paper_authors.user: chuzheng
paper_authors._id: 667a5e85becec8fc5106bab1
paper_authors.name: Shijie Zhang
paper_authors._id: 667a5e85becec8fc5106bab2
paper_authors.name: Jinkun Hao
paper_authors._id: 667a5e85becec8fc5106bab3
paper_authors.name: Junwei Zhu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-25T13:41:52.718Z
paper_authors._id: 65aadb2b9aba49e1d03cfccc
paper_authors.avatarUrl: /avatars/b7c48dc999bf2f94018315b546d50eeb.svg
paper_authors.fullname: Junwei Zhu
paper_authors.type: user
paper_authors.user: ktpt123
paper_authors._id: 667a5e85becec8fc5106bab4
paper_authors.name: Dongjin Huang
paper_title: ClotheDreamer: Text-Guided Garment Generation with 3D Gaussians
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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