title: Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side
    Images
summary: Recent advances in 3D AIGC have shown promise in directly creating 3D objects
  from text and images, offering significant cost savings in animation and
  product design. However, detailed edit and customization of 3D assets remains a
  long-standing challenge. Specifically, 3D Generation methods lack the ability
  to follow finely detailed instructions as precisely as their 2D image creation
  counterparts. Imagine you can get a toy through 3D AIGC but with undesired
  accessories and dressing. To tackle this challenge, we propose a novel pipeline
  called Tailor3D, which swiftly creates customized 3D assets from editable
  dual-side images. We aim to emulate a tailor's ability to locally change
  objects or perform overall style transfer. Unlike creating 3D assets from
  multiple views, using dual-side images eliminates conflicts on overlapping
  areas that occur when editing individual views. Specifically, it begins by
  editing the front view, then generates the back view of the object through
  multi-view diffusion. Afterward, it proceeds to edit the back views. Finally, a
  Dual-sided LRM is proposed to seamlessly stitch together the front and back 3D
  features, akin to a tailor sewing together the front and back of a garment. The
  Dual-sided LRM rectifies imperfect consistencies between the front and back
  views, enhancing editing capabilities and reducing memory burdens while
  seamlessly integrating them into a unified 3D representation with the LoRA
  Triplane Transformer. Experimental results demonstrate Tailor3D's effectiveness
  across various 3D generation and editing tasks, including 3D generative fill
  and style transfer. It provides a user-friendly, efficient solution for editing
  3D assets, with each editing step taking only seconds to complete.
paper_authors._id: 668cb6cc61b6eff5a82aeba2
paper_authors.name: Zhangyang Qi
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paper_authors.fullname: Qi Zhangyang (Alex Severns)
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paper_authors.name: Yunhan Yang
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paper_authors.fullname: Yunhan Yang
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paper_authors.name: Mengchen Zhang
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paper_authors.fullname: Zhang Mengchen
paper_authors.type: user
paper_authors.user: Dubhe-zmc
paper_authors._id: 668cb6cc61b6eff5a82aeba5
paper_authors.name: Long Xing
paper_authors._id: 668cb6cc61b6eff5a82aeba6
paper_authors.name: Xiaoyang Wu
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paper_authors.fullname: Xiaoyang Wu
paper_authors.type: user
paper_authors.user: Gofinge
paper_authors._id: 668cb6cc61b6eff5a82aeba7
paper_authors.name: Tong Wu
paper_authors.status: claimed_verified
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paper_authors.fullname: Tong Wu
paper_authors.type: user
paper_authors.user: wutong16
paper_authors._id: 668cb6cc61b6eff5a82aeba8
paper_authors.name: Dahua Lin
paper_authors.status: admin_assigned
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paper_authors.fullname: Dahua Lin
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paper_authors.name: Xihui Liu
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paper_authors.fullname: Xihui Liu
paper_authors.type: user
paper_authors.user: XihuiLiu
paper_authors._id: 668cb6cc61b6eff5a82aebaa
paper_authors.name: Jiaqi Wang
paper_authors.status: admin_assigned
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paper_authors.fullname: JIaqi
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paper_authors.user: Jiaqiwang
paper_authors._id: 668cb6cc61b6eff5a82aebab
paper_authors.name: Hengshuang Zhao
paper_title: Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side
    Images
paper_summary: Recent advances in 3D AIGC have shown promise in directly creating 3D objects
  from text and images, offering significant cost savings in animation and
  product design. However, detailed edit and customization of 3D assets remains a
  long-standing challenge. Specifically, 3D Generation methods lack the ability
  to follow finely detailed instructions as precisely as their 2D image creation
  counterparts. Imagine you can get a toy through 3D AIGC but with undesired
  accessories and dressing. To tackle this challenge, we propose a novel pipeline
  called Tailor3D, which swiftly creates customized 3D assets from editable
  dual-side images. We aim to emulate a tailor's ability to locally change
  objects or perform overall style transfer. Unlike creating 3D assets from
  multiple views, using dual-side images eliminates conflicts on overlapping
  areas that occur when editing individual views. Specifically, it begins by
  editing the front view, then generates the back view of the object through
  multi-view diffusion. Afterward, it proceeds to edit the back views. Finally, a
  Dual-sided LRM is proposed to seamlessly stitch together the front and back 3D
  features, akin to a tailor sewing together the front and back of a garment. The
  Dual-sided LRM rectifies imperfect consistencies between the front and back
  views, enhancing editing capabilities and reducing memory burdens while
  seamlessly integrating them into a unified 3D representation with the LoRA
  Triplane Transformer. Experimental results demonstrate Tailor3D's effectiveness
  across various 3D generation and editing tasks, including 3D generative fill
  and style transfer. It provides a user-friendly, efficient solution for editing
  3D assets, with each editing step taking only seconds to complete.
paper_ai_keywords: multi-view diffusion Dual-sided LRM LoRA Triplane Transformer 3D generative fill style transfer