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The design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture. Generative models provide a new paradigm for materials design by directly…

Recent advances in diffusion models have led to impressive image generation capabilities, but aligning these models with human preferences remains challenging. Reward-based fine-tuning using models trained on human feedback improves…

Computer Vision and Pattern Recognition · Computer Science 2025-05-29 Dmitrii Sorokin , Maksim Nakhodnov , Andrey Kuznetsov , Aibek Alanov

Diffusion models are able to generate photorealistic images in arbitrary scenes. However, when applying diffusion models to image translation, there exists a trade-off between maintaining spatial structure and high-quality content. Besides,…

Computer Vision and Pattern Recognition · Computer Science 2023-02-07 Shiqi Sun , Shancheng Fang , Qian He , Wei Liu

Deep generative models dominate the existing literature in layout pattern generation. However, leaving the guarantee of legality to an inexplicable neural network could be problematic in several applications. In this paper, we propose…

Computer Vision and Pattern Recognition · Computer Science 2023-03-24 Zixiao Wang , Yunheng Shen , Wenqian Zhao , Yang Bai , Guojin Chen , Farzan Farnia , Bei Yu

Deep generative models have garnered significant attention in low-level vision tasks due to their generative capabilities. Among them, diffusion model-based solutions, characterized by a forward diffusion process and a reverse denoising…

Computer Vision and Pattern Recognition · Computer Science 2025-02-26 Chunming He , Yuqi Shen , Chengyu Fang , Fengyang Xiao , Longxiang Tang , Yulun Zhang , Wangmeng Zuo , Zhenhua Guo , Xiu Li

The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes.…

Thanks to the rapid development of diffusion models, unprecedented progress has been witnessed in image synthesis. Prior works mostly rely on pre-trained linguistic models, but a text is often too abstract to properly specify all the…

Computer Vision and Pattern Recognition · Computer Science 2023-08-15 Binbin Yang , Yi Luo , Ziliang Chen , Guangrun Wang , Xiaodan Liang , Liang Lin

Generative models have advanced significantly in realistic image synthesis, with diffusion models excelling in quality and stability. Recent multi-view diffusion models improve 3D-aware street view generation, but they struggle to produce…

Computer Vision and Pattern Recognition · Computer Science 2026-02-13 Ji Li , Zhiwei Li , Shihao Li , Zhenjiang Yu , Boyang Wang , Haiou Liu

High-quality material generation is key for virtual environment authoring and inverse rendering. We propose MaterialPicker, a multi-modal material generator leveraging a Diffusion Transformer (DiT) architecture, improving and simplifying…

Computer Vision and Pattern Recognition · Computer Science 2025-07-29 Xiaohe Ma , Valentin Deschaintre , Miloš Hašan , Fujun Luan , Kun Zhou , Hongzhi Wu , Yiwei Hu

Diffusion-based 3D generation has made remarkable progress in recent years. However, existing 3D generative models often produce overly dense and unstructured meshes, which stand in stark contrast to the compact, structured, and…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Yuan Li , Cheng Lin , Yuan Liu , Xiaoxiao Long , Chenxu Zhang , Ningna Wang , Xin Li , Wenping Wang , Xiaohu Guo

The three areas of realistic forward rendering, per-pixel inverse rendering, and generative image synthesis may seem like separate and unrelated sub-fields of graphics and vision. However, recent work has demonstrated improved estimation of…

Computer Vision and Pattern Recognition · Computer Science 2024-05-02 Zheng Zeng , Valentin Deschaintre , Iliyan Georgiev , Yannick Hold-Geoffroy , Yiwei Hu , Fujun Luan , Ling-Qi Yan , Miloš Hašan

How do diffusion generative models convert pure noise into meaningful images? In a variety of pretrained diffusion models (including conditional latent space models like Stable Diffusion), we observe that the reverse diffusion process that…

Computer Vision and Pattern Recognition · Computer Science 2024-03-27 Binxu Wang , John J. Vastola

Recent advances in diffusion generative models have yielded remarkable progress. While the quality of generated content continues to improve, these models have grown considerably in size and complexity. This increasing computational burden…

Machine Learning · Computer Science 2025-03-13 Reza Shirkavand , Peiran Yu , Shangqian Gao , Gowthami Somepalli , Tom Goldstein , Heng Huang

Graphic layouts serve as an important and engaging medium for visual communication across different channels. While recent layout generation models have demonstrated impressive capabilities, they frequently fail to align with nuanced human…

Computer Vision and Pattern Recognition · Computer Science 2026-03-02 Varun Gopal , Rishabh Jain , Aradhya Mathur , Nikitha SR , Sohan Patnaik , Sudhir Yarram , Mayur Hemani , Balaji Krishnamurthy , Mausoom Sarkar

Recent diffusion-based generative models show promise in their ability to generate text images, but limitations in specifying the styles of the generated texts render them insufficient in the realm of typographic design. This paper proposes…

Computer Vision and Pattern Recognition · Computer Science 2024-02-23 KhayTze Peong , Seiichi Uchida , Daichi Haraguchi

We present PosterIQ, a design-driven benchmark for poster understanding and generation, annotated across composition structure, typographic hierarchy, and semantic intent. It includes 7,765 image-annotation instances and 822 generation…

Computer Vision and Pattern Recognition · Computer Science 2026-03-26 Yuheng Feng , Wen Zhang , Haodong Duan , Xingxing Zou

Diffusion models have shown the great potential in the point cloud registration (PCR) task, especially for enhancing the robustness to challenging cases. However, existing diffusion-based PCR methods primarily focus on instance-level…

Computer Vision and Pattern Recognition · Computer Science 2024-11-26 Linrui Gong , Jiuming Liu , Junyi Ma , Lihao Liu , Yaonan Wang , Hesheng Wang

In Virtual Product Placement (VPP) applications, the discrete integration of specific brand products into images or videos has emerged as a challenging yet important task. This paper introduces a novel three-stage fully automated VPP…

Computer Vision and Pattern Recognition · Computer Science 2024-05-03 Mohammad Mahmudul Alam , Negin Sokhandan , Emmett Goodman

Reconstructing a renderable 3D model from images is a useful but challenging task. Recent feedforward 3D reconstruction methods have demonstrated remarkable success in efficiently recovering geometry, but still cannot accurately model the…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Zekai Gu , Shuoxuan Feng , Yansong Wang , Hanzhuo Huang , Zhongshuo Du , Chengfeng Zhao , Chengwei Ren , Peng Wang , Yuan Liu

While impressive performance has been achieved in image captioning, the limited diversity of the generated captions and the large parameter scale remain major barriers to the real-word application of these systems. In this work, we propose…

Computer Vision and Pattern Recognition · Computer Science 2023-10-18 Guisheng Liu , Yi Li , Zhengcong Fei , Haiyan Fu , Xiangyang Luo , Yanqing Guo