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Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g., faces on the back view) and inaccurate shapes (e.g., animals with extra…

Computer Vision and Pattern Recognition · Computer Science 2024-03-15 Cheng Chen , Xiaofeng Yang , Fan Yang , Chengzeng Feng , Zhoujie Fu , Chuan-Sheng Foo , Guosheng Lin , Fayao Liu

We present a novel method for 3D scene editing using diffusion models, designed to ensure view consistency and realism across perspectives. Our approach leverages attention features extracted from a single reference image to define the…

Computer Vision and Pattern Recognition · Computer Science 2024-12-12 Eyal Gomel , Lior Wolf

Diffusion models usher a new era of video editing, flexibly manipulating the video contents with text prompts. Despite the widespread application demand in editing human-centered videos, these models face significant challenges in handling…

Computer Vision and Pattern Recognition · Computer Science 2024-08-15 Xiaojing Zhong , Xinyi Huang , Xiaofeng Yang , Guosheng Lin , Qingyao Wu

Model customization introduces new concepts to existing text-to-image models, enabling the generation of these new concepts/objects in novel contexts. However, such methods lack accurate camera view control with respect to the new object,…

Computer Vision and Pattern Recognition · Computer Science 2024-12-04 Nupur Kumari , Grace Su , Richard Zhang , Taesung Park , Eli Shechtman , Jun-Yan Zhu

Generating high-quality 3D objects from textual descriptions remains a challenging problem due to computational cost, the scarcity of 3D data, and complex 3D representations. We introduce Geometry Image Diffusion (GIMDiffusion), a novel…

Computer Vision and Pattern Recognition · Computer Science 2024-09-06 Slava Elizarov , Ciara Rowles , Simon Donné

We tackle the dual challenges of video understanding and controllable video generation within a unified diffusion framework. Our key insights are two-fold: geometry-only cues (e.g., depth, edges) are insufficient: they specify layout but…

Computer Vision and Pattern Recognition · Computer Science 2025-11-27 Dianbing Xi , Jiepeng Wang , Yuanzhi Liang , Xi Qiu , Jialun Liu , Hao Pan , Yuchi Huo , Rui Wang , Haibin Huang , Chi Zhang , Xuelong Li

We present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture…

Computer Vision and Pattern Recognition · Computer Science 2023-10-27 Jingxiang Sun , Bo Zhang , Ruizhi Shao , Lizhen Wang , Wen Liu , Zhenda Xie , Yebin Liu

Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge,…

Computer Vision and Pattern Recognition · Computer Science 2023-02-17 Omer Bar-Tal , Lior Yariv , Yaron Lipman , Tali Dekel

Manipulating transparent objects presents significant challenges due to the complexities introduced by their reflection and refraction properties, which considerably hinder the accurate estimation of their 3D shapes. To address these…

Computer Vision and Pattern Recognition · Computer Science 2025-03-18 Haoxiao Wang , Kaichen Zhou , Binrui Gu , Zhiyuan Feng , Weijie Wang , Peilin Sun , Yicheng Xiao , Jianhua Zhang , Hao Dong

We present a diffusion-based video editing framework, namely DiffusionAtlas, which can achieve both frame consistency and high fidelity in editing video object appearance. Despite the success in image editing, diffusion models still…

Computer Vision and Pattern Recognition · Computer Science 2023-12-08 Shao-Yu Chang , Hwann-Tzong Chen , Tyng-Luh Liu

Multi-view generation with camera pose control and prompt-based customization are both essential elements for achieving controllable generative models. However, existing multi-view generation models do not support customization with…

Computer Vision and Pattern Recognition · Computer Science 2026-03-12 Minjung Shin , Hyunin Cho , Sooyeon Go , Jin-Hwa Kim , Youngjung Uh

State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth. However, an essential aspect often overlooked is the specific camera geometry used during image…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Andrey Voynov , Amir Hertz , Moab Arar , Shlomi Fruchter , Daniel Cohen-Or

Controllable spherical panoramic image generation holds substantial applicative potential across a variety of domains.However, it remains a challenging task due to the inherent spherical distortion and geometry characteristics, resulting in…

Computer Vision and Pattern Recognition · Computer Science 2024-03-18 Tao Wu , Xuewei Li , Zhongang Qi , Di Hu , Xintao Wang , Ying Shan , Xi Li

Recent controllable generation approaches such as FreeControl and Diffusion Self-Guidance bring fine-grained spatial and appearance control to text-to-image (T2I) diffusion models without training auxiliary modules. However, these methods…

Computer Vision and Pattern Recognition · Computer Science 2024-12-12 Kuan Heng Lin , Sicheng Mo , Ben Klingher , Fangzhou Mu , Bolei Zhou

Diffusion Handles is a novel approach to enabling 3D object edits on diffusion images. We accomplish these edits using existing pre-trained diffusion models, and 2D image depth estimation, without any fine-tuning or 3D object retrieval. The…

Computer Vision and Pattern Recognition · Computer Science 2023-12-08 Karran Pandey , Paul Guerrero , Matheus Gadelha , Yannick Hold-Geoffroy , Karan Singh , Niloy Mitra

Despite significant advances in modeling image priors via diffusion models, 3D-aware image editing remains challenging, in part because the object is only specified via a single image. To tackle this challenge, we propose 3D-Fixup, a new…

Computer Vision and Pattern Recognition · Computer Science 2025-05-16 Yen-Chi Cheng , Krishna Kumar Singh , Jae Shin Yoon , Alex Schwing , Liangyan Gui , Matheus Gadelha , Paul Guerrero , Nanxuan Zhao

Manipulating the material appearance of objects in images is critical for applications like augmented reality, virtual prototyping, and digital content creation. We present MaterialFusion, a novel framework for high-quality material…

Computer Vision and Pattern Recognition · Computer Science 2025-02-13 Kamil Garifullin , Maxim Nikolaev , Andrey Kuznetsov , Aibek Alanov

Image tiling -- the seamless connection of disparate images to create a coherent visual field -- is crucial for applications such as texture creation, video game asset development, and digital art. Traditionally, tiles have been constructed…

Computer Vision and Pattern Recognition · Computer Science 2025-03-18 Or Madar , Ohad Fried

Recent advancements in diffusion models have set new benchmarks in image and video generation, enabling realistic visual synthesis across single- and multi-frame contexts. However, these models still struggle with efficiently and explicitly…

Computer Vision and Pattern Recognition · Computer Science 2024-12-03 Qihang Zhang , Shuangfei Zhai , Miguel Angel Bautista , Kevin Miao , Alexander Toshev , Joshua Susskind , Jiatao Gu

Recently, several works tackled the video editing task fostered by the success of large-scale text-to-image generative models. However, most of these methods holistically edit the frame using the text, exploiting the prior given by…

Computer Vision and Pattern Recognition · Computer Science 2024-01-08 Elia Peruzzo , Vidit Goel , Dejia Xu , Xingqian Xu , Yifan Jiang , Zhangyang Wang , Humphrey Shi , Nicu Sebe