TIP-Editor: An Accurate 3D Editor Following Both Text-Prompts And Image-Prompts
Jingyu Zhuang, Di Kang, Yan-Pei Cao, Guanbin Li, Liang Lin, Ying Shan
摘要
Text-driven 3D scene editing has gained significant attention owing to its convenience and user-friendliness. However, existing methods still lack accurate control of the specified appearance and location of the editing result due to the inherent limitations of the text description. To this end, we propose a 3D scene editing framework, TIP-Editor, that accepts both text and image prompts and a 3D bounding box to specify the editing region. With the image prompt, users can conveniently specify the detailed appearance/style of the target content in complement to the text description, enabling accurate control of the appearance. Specifically, TIP-Editor employs a stepwise 2D personalization strategy to better learn the representation of the existing scene and the reference image, in which a localization loss is proposed to encourage correct object placement as specified by the bounding box. Additionally, TIP-Editor utilizes explicit and flexible 3D Gaussian splatting (GS) as the 3D representation to facilitate local editing while keeping the background unchanged. Extensive experiments have demonstrated that TIP-Editor conducts accurate editing following the text and image prompts in the specified bounding box region, consistently outperforming the baselines in editing quality, and the alignment to the prompts, qualitatively and quantitatively.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper22
- Drag Your Gaussian: Effective Drag-Based Editing with Score Distillation for 3D Gaussian SplattingYansong Qu, Dian Chen, Xinyang Li, Xiaofan Li 等SIGGRAPH 2025 · 被引用 14 次
- Thing2Reality: Enabling Spontaneous Creation of 3D Objects from 2D Content using Generative AI in XR MeetingsErzhen Hu, Mingyi Li, Jungtaek Hong, Xun Qian 等UIST 2025 · 被引用 13 次
- Pro3D-Editor: A Progressive-Views Perspective for Consistent and Precise 3D EditingYang Zheng, Mengqi Huang, Nan Chen, Zhendong MaoNeurIPS 2025 · 被引用 11 次
- AdLift: Lifting Adversarial Perturbations to Safeguard 3D Gaussian Splatting Assets Against Instruction-Driven EditingZiming Hong, Tianyu Huang, Runnan Chen, Shanshan Ye 等ICML 2026 · 被引用 10 次
- TINKER: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene OptimizationCanyu Zhao, Xiaoman Li, Tianjian Feng, Zhiyue Zhao 等ICLR 2026 · 被引用 9 次
它引用的顶会 Paper45
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu 等ICLR 2022 · 被引用 18,833 次
- Segment AnythingAlexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao 等ICCV 2023 · 被引用 13,211 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li 等NeurIPS 2022 · 被引用 8,965 次
相关 Paper
- 3D Gaussian Editing with A Single ImageGuan Luo, Tian-Xing Xu, Ying-Tian Liu, Xiaoxiong Fan 等ACM MM 2024 · 被引用 7 次
- GaussianEditor: Editing 3D Gaussians Delicately with Text InstructionsJunjie Wang, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie 等CVPR 2024 · 被引用 65 次
- Edit3D: Elevating 3D Scene Editing with Attention-Driven Multi-Turn InteractivityPeng Zhou, Dunbo Cai, Yujian Du, Runqing Zhang 等ACM MM 2024 · 被引用 3 次
- 3DitScene: Editing Any Scene via Language-guided Disentangled Gaussian SplattingQihang Zhang, Yinghao Xu, Chaoyang Wang, Hsin-Ying Lee 等ICLR 2025
- Morpheus: Text-Driven 3D Gaussian Splat Shape and Color StylizationJamie Wynn, Zawar Qureshi, Jakub Powierza, Jamie Watson 等CVPR 2025
