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Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images

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arxiv 2407.06191 v1 pith:ISHOBB42 submitted 2024-07-08 cs.CV

Zhangyang Qi , Yunhan Yang , Mengchen Zhang , Long Xing , Xiaoyang Wu , Tong Wu , Dahua Lin , Xihui Liu
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Jiaqi Wang Hengshuang Zhao
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classification cs.CV
keywords editingassetsbackfrontimagesviewsdual-sidegeneration
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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.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CompoSE: Compositional Synthesis and Editing of 3D Shapes via Part-Aware Control

    cs.GR 2026-05 unverdicted novelty 7.0 of 10

    CompoSE synthesizes part-separated 3D objects from coarse geometric primitives via a part-aware diffusion transformer, enabling compositional editing operations like substitution and resizing without part-level text prompts.

  2. Hunyuan3D-Buffalo 1.0: A Unified Multimodal Model for Scalable 3D Generation, Understanding, and Editing

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A unified 3D multimodal model combines understanding, text-to-3D generation, instruction-guided editing, and part generation in one architecture, trained on an 87M-sample corpus, with claimed state-of-the-art results.

  3. UMI3D: Robust 3D Generation on Unconstrained Multi-Image Inputs via Simultaneous Focus Cross-Attention Routing

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Routing each 3D voxel to its most informative conditioning image via a model-intrinsic Voxel Reference Score unlocks robust unconstrained multi-image 3D generation without retraining.

  4. EditVerse3D: High-Quality 3D Object Editing with Region-Aware Learning

    cs.CV 2026-07 conditional novelty 6.0 of 10

    An end-to-end 3D editing framework achieves high-fidelity local edits from coarse bounding boxes and 2D image prompts using region-aware loss reweighting and a large-scale parts-derived training dataset.

  5. Edit360: 2D Image Edits to 3D Assets from Any Angle

    cs.GR 2025-06 conditional novelty 6.0 of 10

    A training-free method that propagates a 2D edit applied at any chosen viewpoint across a full 360-degree orbit by fusing anchor-view and front-view video-diffusion trajectories.

  6. Pro3D-Editor : A Progressive-Views Perspective for Consistent and Precise 3D Editing

    cs.GR 2025-05 conditional novelty 6.0 of 10

    Pro3D-Editor chooses the most editing-salient view, propagates the edit to other key views with per-view LoRA experts, and refines the 3D scene, improving multi-view consistency.

  7. CNS-Edit++: Category-Agnostic 3D Editing with Coupled Neural Shape Representation

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Coupling a global latent code with a 3D feature volume lets off-the-shelf 3D generators perform local semantic edits — copy, delete, resize, mix, and drag — across object categories while preserving unedited regions.

  8. SplatPainter: Interactive Authoring of 3D Gaussians from 2D Edits via Test-Time Training

    cs.CV 2025-12 conditional novelty 5.0 of 10

    A test-time-trained feedforward model that propagates 2D edits onto 3D Gaussian attributes at interactive speeds.

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