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Video generation technologies are developing rapidly and have broad potential applications. Among these technologies, camera control is crucial for generating professional-quality videos that accurately meet user expectations. However,…

Computer Vision and Pattern Recognition · Computer Science 2025-03-03 Wanquan Feng , Jiawei Liu , Pengqi Tu , Tianhao Qi , Mingzhen Sun , Tianxiang Ma , Songtao Zhao , Siyu Zhou , Qian He

State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth. However, an essential aspect often overlooked is the specific camera geometry used during image…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Andrey Voynov , Amir Hertz , Moab Arar , Shlomi Fruchter , Daniel Cohen-Or

In this work, we rethink the approach to video super-resolution by introducing a method based on the Diffusion Posterior Sampling framework, combined with an unconditional video diffusion transformer operating in latent space. The video…

Computer Vision and Pattern Recognition · Computer Science 2025-11-05 Zhihao Zhan , Wang Pang , Xiang Zhu , Yechao Bai

360{\deg} videos have emerged as a promising medium to represent our dynamic visual world. Compared to the "tunnel vision" of standard cameras, their borderless field of view offers a more complete perspective of our surroundings. While…

Computer Vision and Pattern Recognition · Computer Science 2026-04-24 Rundong Luo , Matthew Wallingford , Ali Farhadi , Noah Snavely , Wei-Chiu Ma

Perpetual view generation aims to synthesize a long-term video corresponding to an arbitrary camera trajectory solely from a single input image. Recent methods commonly utilize a pre-trained text-to-image diffusion model to synthesize new…

Computer Vision and Pattern Recognition · Computer Science 2025-06-24 Bo Pan , Yang Chen , Yingwei Pan , Ting Yao , Wei Chen , Tao Mei

Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial…

Computer Vision and Pattern Recognition · Computer Science 2022-06-24 Jonathan Ho , Tim Salimans , Alexey Gritsenko , William Chan , Mohammad Norouzi , David J. Fleet

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Shengqu Cai , Duygu Ceylan , Matheus Gadelha , Chun-Hao Paul Huang , Tuanfeng Yang Wang , Gordon Wetzstein

Lifting perspective images and videos to 360{\deg} panoramas enables immersive 3D world generation. Existing approaches often rely on explicit geometric alignment between the perspective and the equirectangular projection (ERP) space. Yet,…

Computer Vision and Pattern Recognition · Computer Science 2026-01-23 Ziyi Wu , Daniel Watson , Andrea Tagliasacchi , David J. Fleet , Marcus A. Brubaker , Saurabh Saxena

Existing video deraining methods are often trained on paired datasets, either synthetic, which limits their ability to generalize to real-world rain, or captured by static cameras, which restricts their effectiveness in dynamic scenes with…

Computer Vision and Pattern Recognition · Computer Science 2026-02-03 Tuomas Varanka , Juan Luis Gonzalez , Hyeongwoo Kim , Pablo Garrido , Xu Yao

Diffusion models are capable of generating impressive images conditioned on text descriptions, and extensions of these models allow users to edit images at a relatively coarse scale. However, the ability to precisely edit the layout,…

Computer Vision and Pattern Recognition · Computer Science 2024-02-01 Daniel Geng , Andrew Owens

Diffusion models excel at 2D outpainting, but extending them to $360^\circ$ panoramic completion from unposed perspective images is challenging due to the geometric and topological mismatch between perspective projections and spherical…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Yuqin Lu , Haofeng Liu , Yang Zhou , Jun Liang , Shengfeng He , Jing Li

Numerous works have recently integrated 3D camera control into foundational text-to-video models, but the resulting camera control is often imprecise, and video generation quality suffers. In this work, we analyze camera motion from a first…

Computer Vision and Pattern Recognition · Computer Science 2025-05-07 Sherwin Bahmani , Ivan Skorokhodov , Guocheng Qian , Aliaksandr Siarohin , Willi Menapace , Andrea Tagliasacchi , David B. Lindell , Sergey Tulyakov

Recent progress in video diffusion models has spurred growing interest in camera-controlled novel-view video generation for dynamic scenes, aiming to provide creators with cinematic camera control capabilities in post-production. A key…

Computer Vision and Pattern Recognition · Computer Science 2025-12-22 Min-Jung Kim , Jeongho Kim , Hoiyeong Jin , Junha Hyung , Jaegul Choo

Modern video codecs and learning-based approaches struggle for semantic reconstruction at extremely low bit-rates due to reliance on low-level spatiotemporal redundancies. Generative models, especially diffusion models, offer a new paradigm…

Image and Video Processing · Electrical Eng. & Systems 2026-02-06 Maojun Zhang , Haotian Wu , Richeng Jin , Deniz Gunduz , Krystian Mikolajczyk

Recent large-scale pre-trained diffusion models have demonstrated a powerful generative ability to produce high-quality videos from detailed text descriptions. However, exerting control over the motion of objects in videos generated by any…

Computer Vision and Pattern Recognition · Computer Science 2025-01-09 Changgu Chen , Junwei Shu , Gaoqi He , Changbo Wang , Yang Li

Humans excel at forecasting the future dynamics of a scene given just a single image. Video generation models that can mimic this ability are an essential component for intelligent systems. Recent approaches have improved temporal coherence…

Computer Vision and Pattern Recognition · Computer Science 2026-05-18 Melonie de Almeida , Daniela Ivanova , Tong Shi , John H. Williamson , Paul Henderson

Advancements in diffusion models have significantly improved video quality, directing attention to fine-grained controllability. However, many existing methods depend on fine-tuning large-scale video models for specific tasks, which becomes…

Computer Vision and Pattern Recognition · Computer Science 2026-03-04 Sangwon Jang , Taekyung Ki , Jaehyeong Jo , Jaehong Yoon , Soo Ye Kim , Zhe Lin , Sung Ju Hwang

The field of generative models has recently witnessed significant progress, with diffusion models showing remarkable performance in image generation. In light of this success, there is a growing interest in exploring the application of…

Computer Vision and Pattern Recognition · Computer Science 2023-06-21 Ariel Lapid , Idan Achituve , Lior Bracha , Ethan Fetaya

We explore a novel video creation experience, namely Video Creation by Demonstration. Given a demonstration video and a context image from a different scene, we generate a physically plausible video that continues naturally from the context…

Computer Vision and Pattern Recognition · Computer Science 2024-12-13 Yihong Sun , Hao Zhou , Liangzhe Yuan , Jennifer J. Sun , Yandong Li , Xuhui Jia , Hartwig Adam , Bharath Hariharan , Long Zhao , Ting Liu

The emergence of diffusion models has greatly propelled the progress in image and video generation. Recently, some efforts have been made in controllable video generation, including text-to-video generation and video motion control, among…

Computer Vision and Pattern Recognition · Computer Science 2024-05-02 Teng Hu , Jiangning Zhang , Ran Yi , Yating Wang , Hongrui Huang , Jieyu Weng , Yabiao Wang , Lizhuang Ma