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We present Stable Video 3D (SV3D) -- a latent video diffusion model for high-resolution, image-to-multi-view generation of orbital videos around a 3D object. Recent work on 3D generation propose techniques to adapt 2D generative models for…

Computer Vision and Pattern Recognition · Computer Science 2024-03-19 Vikram Voleti , Chun-Han Yao , Mark Boss , Adam Letts , David Pankratz , Dmitry Tochilkin , Christian Laforte , Robin Rombach , Varun Jampani

Modern generative video models excel at producing convincing, high-quality outputs, but struggle to maintain multi-view and spatiotemporal consistency in highly dynamic real-world environments. In this work, we introduce \textbf{AnyView}, a…

Recent breakthroughs in text-to-image generation has shown encouraging results via large generative models. Due to the scarcity of 3D assets, it is hardly to transfer the success of text-to-image generation to that of text-to-3D generation.…

Computer Vision and Pattern Recognition · Computer Science 2023-11-28 Yiming Chen , Zhiqi Li , Peidong Liu

We present MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions. Our approach uses a 4D dynamic Neural Radiance Field (NeRF), which is optimized for scene appearance, density, and motion…

Computer Vision and Pattern Recognition · Computer Science 2023-01-27 Uriel Singer , Shelly Sheynin , Adam Polyak , Oron Ashual , Iurii Makarov , Filippos Kokkinos , Naman Goyal , Andrea Vedaldi , Devi Parikh , Justin Johnson , Yaniv Taigman

We present Turbo3D, an ultra-fast text-to-3D system capable of generating high-quality Gaussian splatting assets in under one second. Turbo3D employs a rapid 4-step, 4-view diffusion generator and an efficient feed-forward Gaussian…

Computer Vision and Pattern Recognition · Computer Science 2024-12-06 Hanzhe Hu , Tianwei Yin , Fujun Luan , Yiwei Hu , Hao Tan , Zexiang Xu , Sai Bi , Shubham Tulsiani , Kai Zhang

We study the problem of synthesizing immersive 3D indoor scenes from one or more images. Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while…

Computer Vision and Pattern Recognition · Computer Science 2022-12-02 Jing Yu Koh , Harsh Agrawal , Dhruv Batra , Richard Tucker , Austin Waters , Honglak Lee , Yinfei Yang , Jason Baldridge , Peter Anderson

Most text-to-3D generators build upon off-the-shelf text-to-image models trained on billions of images. They use variants of Score Distillation Sampling (SDS), which is slow, somewhat unstable, and prone to artifacts. A mitigation is to…

Computer Vision and Pattern Recognition · Computer Science 2024-02-14 Luke Melas-Kyriazi , Iro Laina , Christian Rupprecht , Natalia Neverova , Andrea Vedaldi , Oran Gafni , Filippos Kokkinos

Existing state-of-the-art novel view synthesis methods rely on either fairly accurate 3D geometry estimation or sampling of the entire space for neural volumetric rendering, which limit the overall efficiency. In order to improve the…

Computer Vision and Pattern Recognition · Computer Science 2022-04-08 Yuemei Zhou , Tao Yu , Zerong Zheng , Ying Fu , Yebin Liu

Text-to-3D generation approaches have advanced significantly by leveraging pretrained 2D diffusion priors, producing high-quality and 3D-consistent outputs. However, they often fail to produce out-of-domain (OOD) or rare concepts, yielding…

Computer Vision and Pattern Recognition · Computer Science 2025-08-25 Yosef Dayani , Omer Benishu , Sagie Benaim

Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, making it easy to create diverse and individual content. However, existing approaches mostly focus on high-quality short video…

Computer Vision and Pattern Recognition · Computer Science 2025-04-17 Roberto Henschel , Levon Khachatryan , Hayk Poghosyan , Daniil Hayrapetyan , Vahram Tadevosyan , Zhangyang Wang , Shant Navasardyan , Humphrey Shi

We introduce a novel diffusion-based video generation method, generating a video showing multiple events given multiple individual sentences from the user. Our method does not require a large-scale video dataset since our method uses a…

Computer Vision and Pattern Recognition · Computer Science 2024-07-17 Gyeongrok Oh , Jaehwan Jeong , Sieun Kim , Wonmin Byeon , Jinkyu Kim , Sungwoong Kim , Sangpil Kim

3D-aware image synthesis aims to generate images of objects from multiple views by learning a 3D representation. However, one key challenge remains: existing approaches lack geometry constraints, hence usually fail to generate multi-view…

Computer Vision and Pattern Recognition · Computer Science 2022-04-14 Xuanmeng Zhang , Zhedong Zheng , Daiheng Gao , Bang Zhang , Pan Pan , Yi Yang

We present a latent diffusion model over 3D scenes, that can be trained using only 2D image data. To achieve this, we first design an autoencoder that maps multi-view images to 3D Gaussian splats, and simultaneously builds a compressed…

Computer Vision and Pattern Recognition · Computer Science 2024-06-21 Paul Henderson , Melonie de Almeida , Daniela Ivanova , Titas Anciukevičius

The challenge of graphically rendering high frame-rate videos on low compute devices can be addressed through periodic prediction of future frames to enhance the user experience in virtual reality applications. This is studied through the…

Computer Vision and Pattern Recognition · Computer Science 2023-09-15 Nagabhushan Somraj , Pranali Sancheti , Rajiv Soundararajan

We present a novel unconditional video generative model designed to address long-term spatial and temporal dependencies, with attention to computational and dataset efficiency. To capture long spatio-temporal dependencies, our approach…

Computer Vision and Pattern Recognition · Computer Science 2024-08-13 Partha Ghosh , Soubhik Sanyal , Cordelia Schmid , Bernhard Schölkopf

Stochastic video prediction models take in a sequence of image frames, and generate a sequence of consecutive future image frames. These models typically generate future frames in an autoregressive fashion, which is slow and requires the…

Computer Vision and Pattern Recognition · Computer Science 2019-04-23 Ananya Kumar , S. M. Ali Eslami , Danilo J. Rezende , Marta Garnelo , Fabio Viola , Edward Lockhart , Murray Shanahan

We propose a learning-based approach for novel view synthesis for multi-camera 360$^{\circ}$ panorama capture rigs. Previous work constructs RGBD panoramas from such data, allowing for view synthesis with small amounts of translation, but…

Computer Vision and Pattern Recognition · Computer Science 2020-08-06 Kai-En Lin , Zexiang Xu , Ben Mildenhall , Pratul P. Srinivasan , Yannick Hold-Geoffroy , Stephen DiVerdi , Qi Sun , Kalyan Sunkavalli , Ravi Ramamoorthi

Existing Multi-Plane Image (MPI) based view-synthesis methods generate an MPI aligned with the input view using a fixed number of planes in one forward pass. These methods produce fast, high-quality rendering of novel views, but rely on…

Computer Vision and Pattern Recognition · Computer Science 2021-07-13 Sushobhan Ghosh , Zhaoyang Lv , Nathan Matsuda , Lei Xiao , Andrew Berkovich , Oliver Cossairt

Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since video content is highly redundant, we argue that naively…

Computer Vision and Pattern Recognition · Computer Science 2024-02-23 Willi Menapace , Aliaksandr Siarohin , Ivan Skorokhodov , Ekaterina Deyneka , Tsai-Shien Chen , Anil Kag , Yuwei Fang , Aleksei Stoliar , Elisa Ricci , Jian Ren , Sergey Tulyakov

Recent advances in video generation demand increasingly efficient training recipes to mitigate escalating computational costs. In this report, we present ContentV, an 8B-parameter text-to-video model that achieves state-of-the-art…

Computer Vision and Pattern Recognition · Computer Science 2025-06-12 Wenfeng Lin , Renjie Chen , Boyuan Liu , Shiyue Yan , Ruoyu Feng , Jiangchuan Wei , Yichen Zhang , Yimeng Zhou , Chao Feng , Jiao Ran , Qi Wu , Zuotao Liu , Mingyu Guo