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Related papers: Instructive3D: Editing Large Reconstruction Models…

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Current instruction-based editing methods, such as InstructPix2Pix, often fail to produce satisfactory results in complex scenarios due to their dependence on the simple CLIP text encoder in diffusion models. To rectify this, this paper…

Computer Vision and Pattern Recognition · Computer Science 2023-12-13 Yuzhou Huang , Liangbin Xie , Xintao Wang , Ziyang Yuan , Xiaodong Cun , Yixiao Ge , Jiantao Zhou , Chao Dong , Rui Huang , Ruimao Zhang , Ying Shan

Much progress has been made in the supervised learning of 3D reconstruction of rigid objects from multi-view images or a video. However, it is more challenging to reconstruct severely deformed objects from a single-view RGB image in an…

Computer Vision and Pattern Recognition · Computer Science 2022-01-25 Jie Mei , Jingxi Yu , Suzanne Romain , Craig Rose , Kelsey Magrane , Graeme LeeSon , Jenq-Neng Hwang

The rapid development of Large Multimodal Models (LMMs) has led to remarkable progress in 2D visual understanding; however, extending these capabilities to 3D scene understanding remains a significant challenge. Existing approaches…

Computer Vision and Pattern Recognition · Computer Science 2025-09-05 Hongpei Zheng , Lintao Xiang , Qijun Yang , Qian Lin , Hujun Yin

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

Previous efforts have managed to generate production-ready 3D assets from text or images. However, these methods primarily employ NeRF or 3D Gaussian representations, which are not adept at producing smooth, high-quality geometries required…

Graphics · Computer Science 2024-10-15 Rengan Xie , Wenting Zheng , Kai Huang , Yizheng Chen , Qi Wang , Qi Ye , Wei Chen , Yuchi Huo

The convergence of generative artificial intelligence and advanced computer vision technologies introduces a groundbreaking approach to transforming textual descriptions into three-dimensional representations. This research proposes a fully…

Computer Vision and Pattern Recognition · Computer Science 2025-03-06 Venkat Kumar R , Deepak Saravanan

Text-to-image models are powerful for producing high-quality images based on given text prompts, but crafting these prompts often requires specialized vocabulary. To address this, existing methods train rewriting models with supervision…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 Hongji Yang , Yucheng Zhou , Wencheng Han , Jianbing Shen

With the growing demands of AI-generated content (AIGC), the need for high-quality, diverse, and scalable data has become increasingly crucial. However, collecting large-scale real-world data remains costly and time-consuming, hindering the…

Computer Vision and Pattern Recognition · Computer Science 2025-08-08 Kunyu Feng , Yue Ma , Xinhua Zhang , Boshi Liu , Yikuang Yuluo , Yinhan Zhang , Runtao Liu , Hongyu Liu , Zhiyuan Qin , Shanhui Mo , Qifeng Chen , Zeyu Wang

We introduce the new task of generating Illustrated Instructions, i.e., visual instructions customized to a user's needs. We identify desiderata unique to this task, and formalize it through a suite of automatic and human evaluation…

Computer Vision and Pattern Recognition · Computer Science 2024-04-16 Sachit Menon , Ishan Misra , Rohit Girdhar

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

Recent advances in diffusion-based text-to-video (T2V) models have demonstrated remarkable progress, but these models still face challenges in generating videos with multiple objects. Most models struggle with accurately capturing complex…

Computer Vision and Pattern Recognition · Computer Science 2025-05-30 Aimon Rahman , Jiang Liu , Ze Wang , Ximeng Sun , Jialian Wu , Xiaodong Yu , Yusheng Su , Vishal M. Patel , Zicheng Liu , Emad Barsoum

In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion model, TEXTure applies an iterative scheme that paints a 3D…

Computer Vision and Pattern Recognition · Computer Science 2023-02-06 Elad Richardson , Gal Metzer , Yuval Alaluf , Raja Giryes , Daniel Cohen-Or

Text-driven 3D indoor scene generation is useful for gaming, the film industry, and AR/VR applications. However, existing methods cannot faithfully capture the room layout, nor do they allow flexible editing of individual objects in the…

Computer Vision and Pattern Recognition · Computer Science 2025-09-25 Chuan Fang , Yuan Dong , Kunming Luo , Xiaotao Hu , Rakesh Shrestha , Ping Tan

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

Recent advances in 3D generation have been remarkable, with methods such as DreamFusion leveraging large-scale text-to-image diffusion-based models to guide 3D object generation. These methods enable the synthesis of detailed and…

Computer Vision and Pattern Recognition · Computer Science 2024-10-23 Bohan Zeng , Shanglin Li , Yutang Feng , Ling Yang , Hong Li , Sicheng Gao , Jiaming Liu , Conghui He , Wentao Zhang , Jianzhuang Liu , Baochang Zhang , Shuicheng Yan

Recent breakthroughs in 3D generation have enabled the synthesis of high-fidelity individual assets. However, generating 3D compositional objects from single images--particularly under occlusions--remains challenging. Existing methods often…

Computer Vision and Pattern Recognition · Computer Science 2026-03-18 Hui Shan , Keyang Luo , Ming Li , Sizhe Zheng , Yanwei Fu , Zhen Chen , Xiangru Huang

Existing instruction-based image editing models perform well with simple, single-step instructions but degrade in realistic scenarios that involve multiple, lengthy, and interdependent directives. A main cause is the scarcity of training…

Computer Vision and Pattern Recognition · Computer Science 2026-03-19 Zhaoyuan Qiu , Ken Chen , Xiangwei Wang , Yu Xia , Sachith Seneviratne , Saman Halgamuge

Narrated instructional videos often show and describe manipulations of similar objects, e.g., repairing a particular model of a car or laptop. In this work we aim to reconstruct such objects and to localize associated narrations in 3D.…

Computer Vision and Pattern Recognition · Computer Science 2021-09-13 Dimitri Zhukov , Ignacio Rocco , Ivan Laptev , Josef Sivic , Johannes L. Schönberger , Bugra Tekin , Marc Pollefeys

Text-guided diffusion models have shown superior performance in image/video generation and editing. While few explorations have been performed in 3D scenarios. In this paper, we discuss three fundamental and interesting problems on this…

Computer Vision and Pattern Recognition · Computer Science 2023-10-13 Gang Li , Heliang Zheng , Chaoyue Wang , Chang Li , Changwen Zheng , Dacheng Tao

Recent developments in Multimodal Large Language Models (MLLMs) have significantly improved Vision-Language (VL) reasoning in 2D domains. However, extending these capabilities to 3D scene understanding remains a major challenge. Existing 3D…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Haijier Chen , Bo Xu , Shoujian Zhang , Haoze Liu , Jiaxuan Lin , Jingrong Wang