Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images
<|reference_start|>Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images: 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.<|reference_end|>
@article{qi2024tailor3d:,
    title={Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side
  Images},
    author={Zhangyang Qi, Yunhan Yang, Mengchen Zhang, Long Xing, Xiaoyang Wu,
  Tong Wu, Dahua Lin, Xihui Liu, Jiaqi Wang, Hengshuang Zhao},
    journal={arXiv preprint arXiv:2407.06191},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06191},
    primaryClass={cs.CV}
}