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3D Content Generation is at the heart of many computer graphics applications, including video gaming, film-making, virtual and augmented reality, etc. This paper proposes a novel deep-learning based approach for automatically generating…

Computer Vision and Pattern Recognition · Computer Science 2026-02-09 Yongzhi Xu , Yonhon Ng , Yifu Wang , Inkyu Sa , Yunfei Duan , Zhenhong Sun , Yang Li , Pan Ji , Hongdong Li

Creating high-quality materials in computer graphics is a challenging and time-consuming task, which requires great expertise. To simplify this process, we introduce MatFuse, a unified approach that harnesses the generative power of…

Computer Vision and Pattern Recognition · Computer Science 2024-11-18 Giuseppe Vecchio , Renato Sortino , Simone Palazzo , Concetto Spampinato

The rapid advancement in image generation models has predominantly been driven by diffusion models, which have demonstrated unparalleled success in generating high-fidelity, diverse images from textual prompts. Despite their success,…

Computer Vision and Pattern Recognition · Computer Science 2024-03-29 Yusuf Dalva , Hidir Yesiltepe , Pinar Yanardag

Generative models are now widely used by graphic designers and artists. Prior works have shown that these models remember and often replicate content from their training data during generation. Hence as their proliferation increases, it has…

Computer Vision and Pattern Recognition · Computer Science 2024-04-02 Gowthami Somepalli , Anubhav Gupta , Kamal Gupta , Shramay Palta , Micah Goldblum , Jonas Geiping , Abhinav Shrivastava , Tom Goldstein

We introduce RealmDreamer, a technique for generating forward-facing 3D scenes from text descriptions. Our method optimizes a 3D Gaussian Splatting representation to match complex text prompts using pretrained diffusion models. Our key…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Jaidev Shriram , Alex Trevithick , Lingjie Liu , Ravi Ramamoorthi

Style-guided texture generation aims to generate a texture that is harmonious with both the style of the reference image and the geometry of the input mesh, given a reference style image and a 3D mesh with its text description. Although…

Computer Vision and Pattern Recognition · Computer Science 2024-11-04 Zhiyu Xie , Yuqing Zhang , Xiangjun Tang , Yiqian Wu , Dehan Chen , Gongsheng Li , Xaogang Jin

Deepfake images are fast becoming a serious concern due to their realism. Diffusion models have recently demonstrated highly realistic visual content generation, which makes them an excellent potential tool for Deepfake generation. To curb…

Computer Vision and Pattern Recognition · Computer Science 2023-09-27 Yunzhuo Chen , Nur Al Hasan Haldar , Naveed Akhtar , Ajmal Mian

Building generic robotic manipulation systems often requires large amounts of real-world data, which can be dificult to collect. Synthetic data generation offers a promising alternative, but limiting the sim-to-real gap requires significant…

Robotics · Computer Science 2024-11-18 Thomas Lips , Francis wyffels

Diffusion methods have been proven to be very effective to generate images while conditioning on a text prompt. However, and although the quality of the generated images is unprecedented, these methods seem to struggle when trying to…

Computer Vision and Pattern Recognition · Computer Science 2023-02-07 Álvaro Barbero Jiménez

Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support tasks required for 3D understanding, such as view-consistent 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-02-22 Titas Anciukevičius , Zexiang Xu , Matthew Fisher , Paul Henderson , Hakan Bilen , Niloy J. Mitra , Paul Guerrero

Recent advances in diffusion models enable many powerful instruments for image editing. One of these instruments is text-driven image manipulations: editing semantic attributes of an image according to the provided text description. %…

Computer Vision and Pattern Recognition · Computer Science 2023-04-11 Nikita Starodubcev , Dmitry Baranchuk , Valentin Khrulkov , Artem Babenko

While diffusion models have significantly advanced the quality of image generation their capability to accurately and coherently render text within these images remains a substantial challenge. Conventional diffusion-based methods for scene…

Computer Vision and Pattern Recognition · Computer Science 2024-09-17 Qilong Zhangli , Jindong Jiang , Di Liu , Licheng Yu , Xiaoliang Dai , Ankit Ramchandani , Guan Pang , Dimitris N. Metaxas , Praveen Krishnan

The text-to-image synthesis by diffusion models has recently shown remarkable performance in generating high-quality images. Although performs well for simple texts, the models may get confused when faced with complex texts that contain…

Computer Vision and Pattern Recognition · Computer Science 2024-01-15 Chang Yu , Junran Peng , Xiangyu Zhu , Zhaoxiang Zhang , Qi Tian , Zhen Lei

Recent conditional image synthesis approaches provide high-quality synthesized images. However, it is still challenging to accurately adjust image contents such as the positions and orientations of objects, and synthesized images often have…

Computer Vision and Pattern Recognition · Computer Science 2021-08-25 Jaebong Jeong , Janghun Jo , Jingdong Wang , Sunghyun Cho , Jaesik Park

We introduce the first method for automatic image generation from scene-level freehand sketches. Our model allows for controllable image generation by specifying the synthesis goal via freehand sketches. The key contribution is an attribute…

Computer Vision and Pattern Recognition · Computer Science 2020-04-08 Chengying Gao , Qi Liu , Qi Xu , Limin Wang , Jianzhuang Liu , Changqing Zou

Text-to-image generation has shown remarkable progress with the emergence of diffusion models. However, these models often generate factually inconsistent images, failing to accurately reflect the factual information and common sense…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Youngsun Lim , Hyunjung Shim

Recently, the multimedia community has witnessed the rise of diffusion models trained on large-scale multi-modal data for visual content creation, particularly in the field of text-to-image generation. In this paper, we propose a new task…

Computer Vision and Pattern Recognition · Computer Science 2023-11-10 Jingwen Chen , Yingwei Pan , Ting Yao , Tao Mei

Generating sketches guided by reference styles requires precise transfer of stroke attributes, such as line thickness, deformation, and texture sparsity, while preserving semantic structure and content fidelity. To this end, we propose…

Computer Vision and Pattern Recognition · Computer Science 2025-10-21 Rui Yang , Huining Li , Yiyi Long , Xiaojun Wu , Shengfeng He

People often create art by following an artistic workflow involving multiple stages that inform the overall design. If an artist wishes to modify an earlier decision, significant work may be required to propagate this new decision forward…

Computer Vision and Pattern Recognition · Computer Science 2020-07-15 Hung-Yu Tseng , Matthew Fisher , Jingwan Lu , Yijun Li , Vladimir Kim , Ming-Hsuan Yang

For an artist or a graphic designer, the spatial layout of a scene is a critical design choice. However, existing text-to-image diffusion models provide limited support for incorporating spatial information. This paper introduces Composite…

Computer Vision and Pattern Recognition · Computer Science 2023-07-27 Vikram Jamwal , Ramaneswaran S
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