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Related papers: Omni-3DEdit: Generalized Versatile 3D Editing in O…

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Instruction-guided 3D editing is a rapidly emerging field with the potential to broaden access to 3D content creation. However, existing methods face critical limitations: optimization-based approaches are prohibitively slow, while…

Computer Vision and Pattern Recognition · Computer Science 2025-11-24 Weiwei Cai , Shuangkang Fang , Weicai Ye , Xin Dong , Yunhan Yang , Xuanyang Zhang , Wei Cheng , Yanpei Cao , Gang Yu , Tao Chen

Instruction-guided image editing methods have demonstrated significant potential by training diffusion models on automatically synthesized or manually annotated image editing pairs. However, these methods remain far from practical,…

Computer Vision and Pattern Recognition · Computer Science 2025-04-29 Cong Wei , Zheyang Xiong , Weiming Ren , Xinrun Du , Ge Zhang , Wenhu Chen

We present Edit3r, a feed-forward framework that reconstructs and edits 3D scenes in a single pass from unposed, view-inconsistent, instruction-edited images. Unlike prior methods requiring per-scene optimization, Edit3r directly predicts…

Computer Vision and Pattern Recognition · Computer Science 2026-01-01 Jiageng Liu , Weijie Lyu , Xueting Li , Yejie Guo , Ming-Hsuan Yang

Text-driven 3D editing enables user-friendly 3D object or scene editing with text instructions. Due to the lack of multi-view consistency priors, existing methods typically resort to employing 2D generation or editing models to process each…

Computer Vision and Pattern Recognition · Computer Science 2025-12-02 Liyi Chen , Ruihuang Li , Guowen Zhang , Pengfei Wang , Lei Zhang

Existing 2D-lifting-based 3D editing methods often encounter challenges related to inconsistency, stemming from the lack of view-consistent 2D editing models and the difficulty of ensuring consistent editing across multiple views. To…

Graphics · Computer Science 2025-11-03 Zeng Tao , Zheng Ding , Zeyuan Chen , Xiang Zhang , Leizhi Li , Zhuowen Tu

Existing 3D editing methods rely on computationally intensive scene-by-scene iterative optimization and suffer from multi-view inconsistency. We propose an effective and feed-forward 3D editing framework based on the TRELLIS generative…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Shimin Hu , Yuanyi Wei , Fei Zha , Yudong Guo , Juyong Zhang

Text-driven 3D scene editing has recently attracted increasing attention. Most existing methods follow a render-edit-optimize pipeline, where multi-view images are rendered from a 3D scene, edited with 2D image editors, and then used to…

Computer Vision and Pattern Recognition · Computer Science 2026-04-27 Pufan Li , Bi'an Du , Shenghe Zheng , Junyi Yao , Wei Hu

Leveraging the priors of 2D diffusion models for 3D editing has emerged as a promising paradigm. However, maintaining multi-view consistency in edited results remains challenging, and the extreme scarcity of 3D-consistent editing paired…

Computer Vision and Pattern Recognition · Computer Science 2026-03-04 Jiyuan Wang , Chunyu Lin , Lei Sun , Zhi Cao , Yuyang Yin , Lang Nie , Zhenlong Yuan , Xiangxiang Chu , Yunchao Wei , Kang Liao , Guosheng Lin

Recent multimodal large language models have achieved strong performance in unified text and image understanding and generation, yet extending such native capability to 3D remains challenging due to limited data. Compared to abundant 2D…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Chongjie Ye , Cheng Cao , Chuanyu Pan , Yiming Hao , Yihao Zhi , Yuanming Hu , Xiaoguang Han

This paper presents Omni-View, which extends the unified multimodal understanding and generation to 3D scenes based on multiview images, exploring the principle that "generation facilitates understanding". Consisting of understanding model,…

Computer Vision and Pattern Recognition · Computer Science 2026-02-02 JiaKui Hu , Shanshan Zhao , Qing-Guo Chen , Xuerui Qiu , Jialun Liu , Zhao Xu , Weihua Luo , Kaifu Zhang , Yanye Lu

Text-to-Image (T2I) diffusion models have recently gained traction for their versatility and user-friendliness in 2D content generation and editing. However, training a diffusion model specifically for 3D scene editing is challenging due to…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Nazmul Karim , Hasan Iqbal , Umar Khalid , Jing Hua , Chen Chen

Knowledge editing for large language models can offer an efficient solution to alter a model's behavior without negatively impacting the overall performance. However, the current approaches encounter issues with limited generalizability…

Computation and Language · Computer Science 2024-04-30 Ningyu Zhang , Bozhong Tian , Siyuan Cheng , Xiaozhuan Liang , Yi Hu , Kouying Xue , Yanjie Gou , Xi Chen , Huajun Chen

3D editing - the task of locally modifying the geometry or appearance of a 3D asset - has wide applications in immersive content creation, digital entertainment, and AR/VR. However, unlike 2D editing, it remains challenging due to the need…

Computer Vision and Pattern Recognition · Computer Science 2025-10-06 Ruihao Xia , Yang Tang , Pan Zhou

While 2D diffusion models have achieved remarkable success in identity-preserving personalization, extending this capability to 3D assets remains a significant challenge due to the complexities of multi-view consistency and spatial control.…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Jinxin Ai , Matthias Nießner , Ziya Erkoç

Existing image editing tools, while powerful, typically disregard the underlying 3D geometry from which the image is projected. As a result, edits made using these tools may become detached from the geometry and lighting conditions that are…

Computer Vision and Pattern Recognition · Computer Science 2023-07-21 Oscar Michel , Anand Bhattad , Eli VanderBilt , Ranjay Krishna , Aniruddha Kembhavi , Tanmay Gupta

Knowledge editing emerges as a crucial technique for efficiently correcting incorrect or outdated knowledge in large language models (LLM). Existing editing methods rely on a rigid mapping from parameter or module modifications to output,…

Machine Learning · Computer Science 2026-02-02 Jiajie Su , Haoyuan Wang , Xiaohua Feng , Yunshan Ma , Xiaobo Xia , Yuyuan Li , Xiaolin Zheng , Jianmao Xiao , Chaochao Chen

3D editing has shown remarkable capability in editing scenes based on various instructions. However, existing methods struggle with achieving intuitive, localized editing, such as selectively making flowers blossom. Drag-style editing has…

Computer Vision and Pattern Recognition · Computer Science 2024-12-19 Chenghao Gu , Zhenzhe Li , Zhengqi Zhang , Yunpeng Bai , Shuzhao Xie , Zhi Wang

We present a novel approach to shape editing, building on recent progress in 3D reconstruction from multi-view images. We formulate shape editing as a conditional reconstruction problem, where the model must reconstruct the input shape with…

Computer Vision and Pattern Recognition · Computer Science 2025-09-16 Will Gao , Dilin Wang , Yuchen Fan , Aljaz Bozic , Tuur Stuyck , Zhengqin Li , Zhao Dong , Rakesh Ranjan , Nikolaos Sarafianos

Transferring appearance to 3D assets using different representations of the appearance object - such as images or text - has garnered interest due to its wide range of applications in industries like gaming, augmented reality, and digital…

Computer Vision and Pattern Recognition · Computer Science 2025-10-21 Sayan Deb Sarkar , Sinisa Stekovic , Vincent Lepetit , Iro Armeni

Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing. Recent work has…

Computation and Language · Computer Science 2022-05-26 Machel Reid , Graham Neubig
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