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Generating long-range, geometrically consistent video presents a fundamental dilemma: while consistency demands strict adherence to 3D geometry in pixel space, state-of-the-art generative models operate most effectively in a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-23 Hanyang Kong , Xingyi Yang , Xiaoxu Zheng , Xinchao Wang

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

Recent advancements in 3D generation are predominantly propelled by improvements in 3D-aware image diffusion models. These models are pretrained on Internet-scale image data and fine-tuned on massive 3D data, offering the capability of…

Computer Vision and Pattern Recognition · Computer Science 2024-10-03 Zeyu Yang , Zijie Pan , Chun Gu , Li Zhang

Traditional fluid dynamics simulation pipelines combine numerical solvers with rendering, producing highly realistic results but at considerable computational cost. Diffusion-based generative video models offer a faster alternative, yet…

Graphics · Computer Science 2026-03-18 Yang Bai , George Eskandar , Ziyuan Liu , Gitta Kutyniok

Recent advances in video diffusion transformers have enabled interactive gaming world models that allow users to explore generated environments over extended horizons. However, existing approaches struggle with precise action control and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-18 Jisu Nam , Yicong Hong , Chun-Hao Paul Huang , Feng Liu , JoungBin Lee , Jiyoung Kim , Siyoon Jin , Yunsung Lee , Jaeyoon Jung , Suhwan Choi , Seungryong Kim , Yang Zhou

Automatically generating photorealistic and self-consistent appearances for untextured 3D models is a critical challenge in digital content creation. The advancement of large-scale video generation models offers a natural approach: directly…

Computer Vision and Pattern Recognition · Computer Science 2026-03-19 Yan Zeng , Haoran Jiang , Kaixin Yao , Qixuan Zhang , Longwen Zhang , Lan Xu , Jingyi Yu

Large-scale video diffusion models achieve impressive visual quality, yet often fail to preserve geometric consistency. Prior approaches improve consistency either by augmenting the generator with additional modules or applying…

Computer Vision and Pattern Recognition · Computer Science 2026-03-30 Zhaochong An , Orest Kupyn , Théo Uscidda , Andrea Colaco , Karan Ahuja , Serge Belongie , Mar Gonzalez-Franco , Marta Tintore Gazulla

Multi-view image generation holds significant application value in computer vision, particularly in domains like 3D reconstruction, virtual reality, and augmented reality. Most existing methods, which rely on extending single images, face…

Computer Vision and Pattern Recognition · Computer Science 2025-11-20 Jiaqi Wu , Yaosen Chen , Shuyuan Zhu

In this paper, we propose VideoFrom3D, a novel framework for synthesizing high-quality 3D scene videos from coarse geometry, a camera trajectory, and a reference image. Our approach streamlines the 3D graphic design workflow, enabling…

Graphics · Computer Science 2025-09-23 Geonung Kim , Janghyeok Han , Sunghyun Cho

Recent advancements in video generation have witnessed significant progress, especially with the rapid advancement of diffusion models. Despite this, their deficiencies in physical cognition have gradually received widespread attention -…

Computer Vision and Pattern Recognition · Computer Science 2025-03-28 Minghui Lin , Xiang Wang , Yishan Wang , Shu Wang , Fengqi Dai , Pengxiang Ding , Cunxiang Wang , Zhengrong Zuo , Nong Sang , Siteng Huang , Donglin Wang

A long-standing question in physical reasoning is whether video-based models need to rely on factorized representations of physical variables in order to make physically accurate predictions, or whether they can implicitly represent such…

Computer Vision and Pattern Recognition · Computer Science 2026-02-10 Sonia Joseph , Quentin Garrido , Randall Balestriero , Matthew Kowal , Thomas Fel , Shahab Bakhtiari , Blake Richards , Mike Rabbat

Recent advances in deep generative modeling have unlocked unprecedented opportunities for video synthesis. In real-world applications, however, users often seek tools to faithfully realize their creative editing intentions with precise and…

Computer Vision and Pattern Recognition · Computer Science 2025-09-29 Yuhao Liu , Tengfei Wang , Fang Liu , Zhenwei Wang , Rynson W. H. Lau

Reference-driven image completion, which restores missing regions in a target view using additional images, is particularly challenging when the target view differs significantly from the references. Existing generative methods rely solely…

Computer Vision and Pattern Recognition · Computer Science 2025-10-06 Beibei Lin , Tingting Chen , Robby T. Tan

Text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models has shown great promise but still suffers from inconsistent 3D geometric structures (Janus problems) and severe artifacts. The aforementioned problems…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Baorui Ma , Haoge Deng , Junsheng Zhou , Yu-Shen Liu , Tiejun Huang , Xinlong Wang

This study introduces an efficient and effective method, MeDM, that utilizes pre-trained image Diffusion Models for video-to-video translation with consistent temporal flow. The proposed framework can render videos from scene position…

Computer Vision and Pattern Recognition · Computer Science 2023-12-21 Ernie Chu , Tzuhsuan Huang , Shuo-Yen Lin , Jun-Cheng Chen

Diffusion models have revolutionized image and video generation, achieving unprecedented visual quality. However, their reliance on transformer architectures incurs prohibitively high computational costs, particularly when extending…

Computer Vision and Pattern Recognition · Computer Science 2025-10-03 Justin Cui , Jie Wu , Ming Li , Tao Yang , Xiaojie Li , Rui Wang , Andrew Bai , Yuanhao Ban , Cho-Jui Hsieh

Recent generative models can create visually plausible 3D representations of objects. However, the generation process often allows for implicit control signals, such as contextual descriptions, and rarely supports bold geometric distortions…

Computer Vision and Pattern Recognition · Computer Science 2026-04-10 Changwoon Choi , Hyunsoo Lee , Clément Jambon , Yael Vinker , Young Min Kim

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

Face animation aims at creating photo-realistic portrait videos with animated poses and expressions. A common practice is to generate displacement fields that are used to warp pixels and features from source to target. However, prior…

Computer Vision and Pattern Recognition · Computer Science 2022-10-06 Yatao Zhong , Faezeh Amjadi , Ilya Zharkov

Recent advances in diffusion-based generation techniques enable AI models to produce highly realistic videos, heightening the need for reliable detection mechanisms. However, existing detection methods provide only limited exploration of…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 Wenhan Chen , Sezer Karaoglu , Theo Gevers