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Molecule generation, especially generating 3D molecular geometries from scratch (i.e., 3D \textit{de novo} generation), has become a fundamental task in drug designs. Existing diffusion-based 3D molecule generation methods could suffer from…

Machine Learning · Computer Science 2022-09-14 Lei Huang , Hengtong Zhang , Tingyang Xu , Ka-Chun Wong

In recent years, there has been a significant surge of interest in unifying image comprehension and generation within Large Language Models (LLMs). This growing interest has prompted us to explore extending this unification to videos. The…

Computer Vision and Pattern Recognition · Computer Science 2024-12-06 Yuying Ge , Yizhuo Li , Yixiao Ge , Ying Shan

Diffusion and flow matching models have unlocked unprecedented capabilities for creative content creation, such as interactive image and streaming video generation. The growing demand for higher resolutions, frame rates, and context…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Brian Chao , Lior Yariv , Howard Xiao , Gordon Wetzstein

Autonomous driving training requires a diverse range of datasets encompassing various traffic conditions, weather scenarios, and road types. Traditional data augmentation methods often struggle to generate datasets that represent rare…

Computer Vision and Pattern Recognition · Computer Science 2024-08-29 Yongjie Fu , Yunlong Li , Xuan Di

Our study introduces a new image-to-video generator called FashionFlow to generate fashion videos. By utilising a diffusion model, we are able to create short videos from still fashion images. Our approach involves developing and connecting…

Computer Vision and Pattern Recognition · Computer Science 2024-01-23 Tasin Islam , Alina Miron , XiaoHui Liu , Yongmin Li

Leveraging text, images, structure maps, or motion trajectories as conditional guidance, diffusion models have achieved great success in automated and high-quality video generation. However, generating smooth and rational transition videos…

Computer Vision and Pattern Recognition · Computer Science 2025-08-05 Zuhao Yang , Jiahui Zhang , Yingchen Yu , Shijian Lu , Song Bai

Human motion generation is a significant pursuit in generative computer vision with widespread applications in film-making, video games, AR/VR, and human-robot interaction. Current methods mainly utilize either diffusion-based generative…

Computer Vision and Pattern Recognition · Computer Science 2025-02-03 Canxuan Gang

Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Recent developments in diffusion-based generative models allow for more realistic…

Computer Vision and Pattern Recognition · Computer Science 2023-08-01 Michał Stypułkowski , Konstantinos Vougioukas , Sen He , Maciej Zięba , Stavros Petridis , Maja Pantic

Diffusion-based text-to-image generation models trade latency for quality: small models are fast but generate lower-quality images, while large models produce better images but are slow. We present MoDM, a novel caching-based serving system…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-08-05 Yuchen Xia , Divyam Sharma , Yichao Yuan , Souvik Kundu , Nishil Talati

Diffusion models have achieved remarkable progress in video generation, but their controllability remains a major limitation. Key scene factors such as layout, lighting, and camera trajectory are often entangled or only weakly modeled,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-10 Ziqi Cai , Taoyu Yang , Zheng Chang , Si Li , Han Jiang , Shuchen Weng , Boxin Shi

Diffusion-based image generators can now produce high-quality and diverse samples, but their success has yet to fully translate to 3D generation: existing diffusion methods can either generate low-resolution but 3D consistent outputs, or…

Computer Vision and Pattern Recognition · Computer Science 2023-08-29 Animesh Karnewar , Niloy J. Mitra , Andrea Vedaldi , David Novotny

Diffusion models are the de facto approach for generating high-quality images and videos, but learning high-dimensional models remains a formidable task due to computational and optimization challenges. Existing methods often resort to…

Computer Vision and Pattern Recognition · Computer Science 2024-09-04 Jiatao Gu , Shuangfei Zhai , Yizhe Zhang , Josh Susskind , Navdeep Jaitly

Given the remarkable achievements in image generation through diffusion models, the research community has shown increasing interest in extending these models to video generation. Recent diffusion models for video generation have…

Computer Vision and Pattern Recognition · Computer Science 2024-09-05 Yuta Oshima , Shohei Taniguchi , Masahiro Suzuki , Yutaka Matsuo

We introduce Efficient Motion Diffusion Model (EMDM) for fast and high-quality human motion generation. Current state-of-the-art generative diffusion models have produced impressive results but struggle to achieve fast generation without…

Computer Vision and Pattern Recognition · Computer Science 2024-11-26 Wenyang Zhou , Zhiyang Dou , Zeyu Cao , Zhouyingcheng Liao , Jingbo Wang , Wenjia Wang , Yuan Liu , Taku Komura , Wenping Wang , Lingjie Liu

This paper presents a novel method for building scalable 3D generative models utilizing pre-trained video diffusion models. The primary obstacle in developing foundation 3D generative models is the limited availability of 3D data. Unlike…

Computer Vision and Pattern Recognition · Computer Science 2024-07-22 Junlin Han , Filippos Kokkinos , Philip Torr

We present a method for generating video sequences with coherent motion between a pair of input key frames. We adapt a pretrained large-scale image-to-video diffusion model (originally trained to generate videos moving forward in time from…

Computer Vision and Pattern Recognition · Computer Science 2025-02-13 Xiaojuan Wang , Boyang Zhou , Brian Curless , Ira Kemelmacher-Shlizerman , Aleksander Holynski , Steven M. Seitz

Video diffusion models have recently achieved remarkable results in video generation. Despite their encouraging performance, most of these models are mainly designed and trained for short video generation, leading to challenges in…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Zhuoling Li , Hossein Rahmani , Qiuhong Ke , Jun Liu

Stereo video generation has been gaining increasing attention with recent advancements in video diffusion models. However, most existing methods focus on generating 3D stereoscopic videos from monocular 2D videos. These approaches typically…

Computer Vision and Pattern Recognition · Computer Science 2025-06-09 Xingchang Huang , Ashish Kumar Singh , Florian Dubost , Cristina Nader Vasconcelos , Sakar Khattar , Liang Shi , Christian Theobalt , Cengiz Oztireli , Gurprit Singh

Video generation has increasingly gained interest in both academia and industry. Although commercial tools can generate plausible videos, there is a limited number of open-source models available for researchers and engineers. In this work,…

Computer Vision and Pattern Recognition · Computer Science 2023-10-31 Haoxin Chen , Menghan Xia , Yingqing He , Yong Zhang , Xiaodong Cun , Shaoshu Yang , Jinbo Xing , Yaofang Liu , Qifeng Chen , Xintao Wang , Chao Weng , Ying Shan

Generating novel views of an object from a single image is a challenging task. It requires an understanding of the underlying 3D structure of the object from an image and rendering high-quality, spatially consistent new views. While recent…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Jeong-gi Kwak , Erqun Dong , Yuhe Jin , Hanseok Ko , Shweta Mahajan , Kwang Moo Yi