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Related papers: Vinedresser3D: Agentic Text-guided 3D Editing

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Text-driven 3D scene generation techniques have made rapid progress in recent years. Their success is mainly attributed to using existing generative models to iteratively perform image warping and inpainting to generate 3D scenes. However,…

Computer Vision and Pattern Recognition · Computer Science 2024-03-15 Frank Zhang , Yibo Zhang , Quan Zheng , Rui Ma , Wei Hua , Hujun Bao , Weiwei Xu , Changqing Zou

Instruction-based image editing aims to modify specific image elements with natural language instructions. However, current models in this domain often struggle to accurately execute complex user instructions, as they are trained on…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Qifan Yu , Wei Chow , Zhongqi Yue , Kaihang Pan , Yang Wu , Xiaoyang Wan , Juncheng Li , Siliang Tang , Hanwang Zhang , Yueting Zhuang

Natural language offers a highly intuitive interface for enabling localized fine-grained edits of 3D shapes. However, prior works face challenges in preserving global coherence while locally modifying the input 3D shape. In this work, we…

Graphics · Computer Science 2025-07-22 Etai Sella , Noam Atia , Ron Mokady , Hadar Averbuch-Elor

This paper proposes a novel and physically interpretable method for face editing based on arbitrary text prompts. Different from previous GAN-inversion-based face editing methods that manipulate the latent space of GANs, or diffusion-based…

Computer Vision and Pattern Recognition · Computer Science 2023-08-14 Yapeng Meng , Songru Yang , Xu Hu , Rui Zhao , Lincheng Li , Zhenwei Shi , Zhengxia Zou

Generative models have enabled intuitive image creation and manipulation using natural language. In particular, diffusion models have recently shown remarkable results for natural image editing. In this work, we propose to apply diffusion…

Drawing upon StyleGAN's expressivity and disentangled latent space, existing 2D approaches employ textual prompting to edit facial images with different attributes. In contrast, 3D-aware approaches that generate faces at different target…

Computer Vision and Pattern Recognition · Computer Science 2024-07-25 Amandeep Kumar , Muhammad Awais , Sanath Narayan , Hisham Cholakkal , Salman Khan , Rao Muhammad Anwer

Identity preserving editing of faces is a generative task that enables modifying the illumination, adding/removing eyeglasses, face aging, editing hairstyles, modifying expression etc., while preserving the identity of the face. Recent…

Computer Vision and Pattern Recognition · Computer Science 2025-10-22 Vishal Vinod

We propose an interactive editing method that allows humans to help deep neural networks (DNNs) learn a latent space more consistent with human knowledge, thereby improving classification accuracy on indistinguishable ambiguous data.…

Machine Learning · Computer Science 2022-12-09 Jiafu Wei , Ding Xia , Haoran Xie , Chia-Ming Chang , Chuntao Li , Xi Yang

Text-guided image editing has recently experienced rapid development. However, simultaneously performing multiple editing actions on a single image, such as background replacement and specific subject attribute changes, while maintaining…

Computer Vision and Pattern Recognition · Computer Science 2024-04-09 Pengzhi Li , QInxuan Huang , Yikang Ding , Zhiheng Li

Generating 3D faces from textual descriptions has a multitude of applications, such as gaming, movie, and robotics. Recent progresses have demonstrated the success of unconditional 3D face generation and text-to-3D shape generation.…

Computer Vision and Pattern Recognition · Computer Science 2023-09-01 Cuican Yu , Guansong Lu , Yihan Zeng , Jian Sun , Xiaodan Liang , Huibin Li , Zongben Xu , Songcen Xu , Wei Zhang , Hang Xu

Text-conditioned diffusion models can generate impressive images, but fall short when it comes to fine-grained control. Unlike direct-editing tools like Photoshop, text conditioned models require the artist to perform "prompt engineering,"…

Computer Vision and Pattern Recognition · Computer Science 2024-04-05 Michelle Shu , Charles Herrmann , Richard Strong Bowen , Forrester Cole , Ramin Zabih

Generative AI has made rapid progress in text, image, and video synthesis, yet text-to-3D modeling for scientific design remains particularly challenging due to limited controllability and high computational cost. Most existing 3D…

Graphics · Computer Science 2026-04-01 Rachel K. Luu , Markus J. Buehler

In the accelerating era of human-instructed visual content creation, diffusion models have demonstrated remarkable generative potential. Yet their deployment is constrained by a dual bottleneck: semantic ambiguity in diverse prompts and the…

Computer Vision and Pattern Recognition · Computer Science 2026-01-21 Jie Qin , Jie Wu , Weifeng Chen , Yueming Lyu

Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution or high-quality ones with no editing flexibility. In this work, we propose a new approach that…

Computer Vision and Pattern Recognition · Computer Science 2022-06-01 Jingxiang Sun , Xuan Wang , Yichun Shi , Lizhen Wang , Jue Wang , Yebin Liu

In recent years, text-guided image manipulation has gained increasing attention in the multimedia and computer vision community. The input to conditional image generation has evolved from image-only to multimodality. In this paper, we study…

Computer Vision and Pattern Recognition · Computer Science 2021-11-30 Tianhao Zhang , Hung-Yu Tseng , Lu Jiang , Weilong Yang , Honglak Lee , Irfan Essa

Speech-driven 3D facial animation aims to generate realistic and expressive facial motions directly from audio. While recent methods achieve high-quality lip synchronization, they often rely on discrete emotion categories, limiting…

Multimedia · Computer Science 2026-01-16 Diqiong Jiang , Kai Zhu , Dan Song , Jian Chang , Chenglizhao Chen , Zhenyu Wu

We introduce the task of 3D visual grounding in large-scale dynamic scenes based on natural linguistic descriptions and online captured multi-modal visual data, including 2D images and 3D LiDAR point clouds. We present a novel method,…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Zhenxiang Lin , Xidong Peng , Peishan Cong , Ge Zheng , Yujin Sun , Yuenan Hou , Xinge Zhu , Sibei Yang , Yuexin Ma

Given the remarkable ability of 2D foundation image models to generate high-fidelity outputs, we investigate a fundamental question: do 2D foundation image models inherently possess 3D world model capabilities? To answer this, we…

Computer Vision and Pattern Recognition · Computer Science 2026-03-23 Ziya Erkoç , Angela Dai , Matthias Nießner

Recent 3D generative models have achieved remarkable performance in synthesizing high resolution photorealistic images with view consistency and detailed 3D shapes, but training them for diverse domains is challenging since it requires…

Computer Vision and Pattern Recognition · Computer Science 2023-04-03 Gwanghyun Kim , Se Young Chun

Vision-language navigation (VLN) requires an agent to navigate through an 3D environment based on visual observations and natural language instructions. It is clear that the pivotal factor for successful navigation lies in the comprehensive…

Computer Vision and Pattern Recognition · Computer Science 2024-03-22 Rui Liu , Wenguan Wang , Yi Yang
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