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We seek to give users precise control over diffusion-based image generation by modeling complex scenes as sequences of layers, which define the desired spatial arrangement and visual attributes of objects in the scene. Collage Diffusion…

Computer Vision and Pattern Recognition · Computer Science 2023-09-01 Vishnu Sarukkai , Linden Li , Arden Ma , Christopher Ré , Kayvon Fatahalian

Video diffusion models generate high-quality and diverse worlds; however, individual frames often lack 3D consistency across the output sequence, which makes the reconstruction of 3D worlds difficult. To this end, we propose a new method…

Computer Vision and Pattern Recognition · Computer Science 2026-03-19 Lukas Höllein , Matthias Nießner

The creation of diverse and realistic driving scenarios has become essential to enhance perception and planning capabilities of the autonomous driving system. However, generating long-duration, surround-view consistent driving videos…

Computer Vision and Pattern Recognition · Computer Science 2025-03-07 Rui Chen , Zehuan Wu , Yichen Liu , Yuxin Guo , Jingcheng Ni , Haifeng Xia , Siyu Xia

Despite having tremendous progress in image-to-3D generation, existing methods still struggle to produce multi-view consistent images with high-resolution textures in detail, especially in the paradigm of 2D diffusion that lacks 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-09-12 Haibo Yang , Yang Chen , Yingwei Pan , Ting Yao , Zhineng Chen , Chong-Wah Ngo , Tao Mei

While text-to-video diffusion models have made significant strides, many still face challenges in generating videos with temporal consistency. Within diffusion frameworks, guidance techniques have proven effective in enhancing output…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Hyelin Nam , Jaemin Kim , Dohun Lee , Jong Chul Ye

Research on video generation has recently made tremendous progress, enabling high-quality videos to be generated from text prompts or images. Adding control to the video generation process is an important goal moving forward and recent…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Zhengfei Kuang , Shengqu Cai , Hao He , Yinghao Xu , Hongsheng Li , Leonidas Guibas , Gordon Wetzstein

Recovering 3D scenes from sparse views is a challenging task due to its inherent ill-posed problem. Conventional methods have developed specialized solutions (e.g., geometry regularization or feed-forward deterministic model) to mitigate…

Computer Vision and Pattern Recognition · Computer Science 2025-04-04 Hanyang Wang , Fangfu Liu , Jiawei Chi , Yueqi Duan

Object manipulation in images aims to not only edit the object's presentation but also gift objects with motion. Previous methods encountered challenges in concurrently handling static editing and dynamic generation, while also struggling…

Computer Vision and Pattern Recognition · Computer Science 2025-01-23 Ruisi Zhao , Zechuan Zhang , Zongxin Yang , Yi Yang

Video generation models have made significant progress in generating realistic content, enabling applications in simulation, gaming, and film making. However, current generated videos still contain visual artifacts arising from 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Duolikun Danier , Ge Gao , Steven McDonagh , Changjian Li , Hakan Bilen , Oisin Mac Aodha

We introduce LightIt, a method for explicit illumination control for image generation. Recent generative methods lack lighting control, which is crucial to numerous artistic aspects of image generation such as setting the overall mood or…

Computer Vision and Pattern Recognition · Computer Science 2024-03-26 Peter Kocsis , Julien Philip , Kalyan Sunkavalli , Matthias Nießner , Yannick Hold-Geoffroy

Material reconstruction from a photograph is a key component of 3D content creation democratization. We propose to formulate this ill-posed problem as a controlled synthesis one, leveraging the recent progress in generative deep networks.…

Computer Vision and Pattern Recognition · Computer Science 2024-08-28 Giuseppe Vecchio , Rosalie Martin , Arthur Roullier , Adrien Kaiser , Romain Rouffet , Valentin Deschaintre , Tamy Boubekeur

Existing diffusion-based 3D scene generation methods primarily operate in 2D image/video latent spaces, which makes maintaining cross-view appearance and geometric consistency inherently challenging. To bridge this gap, we present OneWorld,…

Computer Vision and Pattern Recognition · Computer Science 2026-03-18 Sensen Gao , Zhaoqing Wang , Qihang Cao , Dongdong Yu , Changhu Wang , Tongliang Liu , Mingming Gong , Jiawang Bian

This paper introduces innovative solutions to enhance spatial controllability in diffusion models reliant on text queries. We first introduce vision guidance as a foundational spatial cue within the perturbed distribution. This…

Computer Vision and Pattern Recognition · Computer Science 2024-12-02 Zipeng Qi , Guoxi Huang , Chenyang Liu , Fei Ye

Precise geometric control in image generation is essential for engineering \& product design and creative industries to control 3D object features accurately in image space. Traditional 3D editing approaches are time-consuming and demand…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Phillip Mueller , Talip Uenlue , Sebastian Schmidt , Marcel Kollovieh , Jiajie Fan , Stephan Guennemann , Lars Mikelsons

We propose EditCrafter, a high-resolution image editing method that operates without tuning, leveraging pretrained text-to-image (T2I) diffusion models to process images at resolutions significantly exceeding those used during training.…

Computer Vision and Pattern Recognition · Computer Science 2026-04-14 Kunho Kim , Sumin Seo , Yongjun Cho , Hyungjin Chung

Existing 2D methods utilize UNet-based diffusion models to generate multi-view physically-based rendering (PBR) maps but struggle with multi-view inconsistency, while some 3D methods directly generate UV maps, encountering generalization…

Computer Vision and Pattern Recognition · Computer Science 2024-12-19 Shenhao Zhu , Lingteng Qiu , Xiaodong Gu , Zhengyi Zhao , Chao Xu , Yuxiao He , Zhe Li , Xiaoguang Han , Yao Yao , Xun Cao , Siyu Zhu , Weihao Yuan , Zilong Dong , Hao Zhu

This paper investigates a solution for enabling in-context capabilities of video diffusion transformers, with minimal tuning required for activation. Specifically, we propose a simple pipeline to leverage in-context generation:…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Zhengcong Fei , Di Qiu , Debang Li , Changqian Yu , Mingyuan Fan

We address the challenge of relighting a single image or video, a task that demands precise scene intrinsic understanding and high-quality light transport synthesis. Existing end-to-end relighting models are often limited by the scarcity of…

Computer Vision and Pattern Recognition · Computer Science 2025-06-19 Kai He , Ruofan Liang , Jacob Munkberg , Jon Hasselgren , Nandita Vijaykumar , Alexander Keller , Sanja Fidler , Igor Gilitschenski , Zan Gojcic , Zian Wang

Recently, 3D reconstruction and generation have demonstrated impressive novel view synthesis results, achieving high fidelity and efficiency. However, a notable conditioning gap can be observed between these two fields, e.g., scalable 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Sibo Wu , Congrong Xu , Binbin Huang , Andreas Geiger , Anpei Chen

Recent 3D large reconstruction models typically employ a two-stage process, including first generate multi-view images by a multi-view diffusion model, and then utilize a feed-forward model to reconstruct images to 3D content.However,…

Computer Vision and Pattern Recognition · Computer Science 2024-07-30 Zhenyu Tang , Junwu Zhang , Xinhua Cheng , Wangbo Yu , Chaoran Feng , Yatian Pang , Bin Lin , Li Yuan