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We present CAT4D, a method for creating 4D (dynamic 3D) scenes from monocular video. CAT4D leverages a multi-view video diffusion model trained on a diverse combination of datasets to enable novel view synthesis at any specified camera…

Computer Vision and Pattern Recognition · Computer Science 2024-12-20 Rundi Wu , Ruiqi Gao , Ben Poole , Alex Trevithick , Changxi Zheng , Jonathan T. Barron , Aleksander Holynski

While diffusion models have demonstrated remarkable progress in 2D image generation and editing, extending these capabilities to 3D editing remains challenging, particularly in maintaining multi-view consistency. Classical approaches…

Computer Vision and Pattern Recognition · Computer Science 2025-08-05 Yufeng Chi , Huimin Ma , Kafeng Wang , Jianmin Li

In this paper, we propose \textbf{CharacterShot}, a controllable and consistent 4D character animation framework that enables any individual designer to create dynamic 3D characters (i.e., 4D character animation) from a single reference…

Computer Vision and Pattern Recognition · Computer Science 2025-08-12 Junyao Gao , Jiaxing Li , Wenran Liu , Yanhong Zeng , Fei Shen , Kai Chen , Yanan Sun , Cairong Zhao

While diffusion-based methods have shown impressive capabilities in capturing diverse and complex hairstyles, their ability to generate consistent and high-quality multi-view outputs -- crucial for real-world applications such as digital…

Computer Vision and Pattern Recognition · Computer Science 2025-07-11 Kuiyuan Sun , Yuxuan Zhang , Jichao Zhang , Jiaming Liu , Wei Wang , Niculae Sebe , Yao Zhao

A recent frontier in computer vision has been the task of 3D video generation, which consists of generating a time-varying 3D representation of a scene. To generate dynamic 3D scenes, current methods explicitly model 3D temporal dynamics by…

Computer Vision and Pattern Recognition · Computer Science 2024-08-01 Rishab Parthasarathy , Zachary Ankner , Aaron Gokaslan

Recently, image-to-video (I2V) diffusion models have demonstrated impressive scene understanding and generative quality, incorporating image conditions to guide generation. However, these models primarily animate static images without…

Computer Vision and Pattern Recognition · Computer Science 2026-05-29 Luis Denninger , Sina Mokhtarzadeh Azar , Juergen Gall

Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. In this work, we propose \textbf{ViewCrafter}, a novel method for synthesizing high-fidelity novel…

Computer Vision and Pattern Recognition · Computer Science 2024-09-04 Wangbo Yu , Jinbo Xing , Li Yuan , Wenbo Hu , Xiaoyu Li , Zhipeng Huang , Xiangjun Gao , Tien-Tsin Wong , Ying Shan , Yonghong Tian

We present a novel framework for free-viewpoint facial performance relighting using diffusion-based image-to-image translation. Leveraging a subject-specific dataset containing diverse facial expressions captured under various lighting…

Computer Vision and Pattern Recognition · Computer Science 2024-10-11 Mingming He , Pascal Clausen , Ahmet Levent Taşel , Li Ma , Oliver Pilarski , Wenqi Xian , Laszlo Rikker , Xueming Yu , Ryan Burgert , Ning Yu , Paul Debevec

Videos express highly structured spatio-temporal patterns of visual data. A video can be thought of as being governed by two factors: (i) temporally invariant (e.g., person identity), or slowly varying (e.g., activity), attribute-induced…

Computer Vision and Pattern Recognition · Computer Science 2018-03-26 Jiawei He , Andreas Lehrmann , Joseph Marino , Greg Mori , Leonid Sigal

Generating long, cohesive video stories with consistent characters is a significant challenge for current text-to-video AI. We introduce a method that approaches video generation in a filmmaker-like manner. Instead of creating a video in…

Computer Vision and Pattern Recognition · Computer Science 2025-12-22 Chayan Jain , Rishant Sharma , Archit Garg , Ishan Bhanuka , Pratik Narang , Dhruv Kumar

Recent advances in diffusion-based generative models have established a new paradigm for image and video relighting. However, extending these capabilities to 4D relighting remains challenging, due primarily to the scarcity of paired 4D…

Computer Vision and Pattern Recognition · Computer Science 2026-02-13 Zhenghuang Wu , Kang Chen , Zeyu Zhang , Hao Tang

While diffusion models have shown impressive performance in 2D image/video generation, diffusion-based Text-to-Multi-view-Video (T2MVid) generation remains underexplored. The new challenges posed by T2MVid generation lie in the lack of…

Computer Vision and Pattern Recognition · Computer Science 2024-06-14 Bing Li , Cheng Zheng , Wenxuan Zhu , Jinjie Mai , Biao Zhang , Peter Wonka , Bernard Ghanem

Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions…

Graphics · Computer Science 2024-02-27 Hongda Jiang , Xi Wang , Marc Christie , Libin Liu , Baoquan Chen

Recent advances in large-scale text-to-image models have revolutionized creative fields by generating visually captivating outputs from textual prompts; however, while traditional photography offers precise control over camera settings to…

Graphics · Computer Science 2025-06-17 Armando Fortes , Tianyi Wei , Shangchen Zhou , Xingang Pan

In this paper, we introduce \textbf{DimensionX}, a framework designed to generate photorealistic 3D and 4D scenes from just a single image with video diffusion. Our approach begins with the insight that both the spatial structure of a 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-11-08 Wenqiang Sun , Shuo Chen , Fangfu Liu , Zilong Chen , Yueqi Duan , Jun Zhang , Yikai Wang

Following the advancements in text-guided image generation technology exemplified by Stable Diffusion, video generation is gaining increased attention in the academic community. However, relying solely on text guidance for video generation…

Computer Vision and Pattern Recognition · Computer Science 2024-09-17 Cong Wang , Jiaxi Gu , Panwen Hu , Haoyu Zhao , Yuanfan Guo , Jianhua Han , Hang Xu , Xiaodan Liang

Diffusion-based video generation techniques have significantly improved zero-shot talking-head avatar generation, enhancing the naturalness of both head motion and facial expressions. However, existing methods suffer from poor…

Graphics · Computer Science 2025-04-24 Lingzhou Mu , Baiji Liu , Ruonan Zhang , Guiming Mo , Jiawei Jin , Kai Zhang , Haozhi Huang

In this paper, we present MVTokenFlow for high-quality 4D content creation from monocular videos. Recent advancements in generative models such as video diffusion models and multiview diffusion models enable us to create videos or 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-02-18 Hanzhuo Huang , Yuan Liu , Ge Zheng , Jiepeng Wang , Zhiyang Dou , Sibei Yang

The demand for realistic and versatile character animation has surged, driven by its wide-ranging applications in various domains. However, the animation generation algorithms modeling human pose with 2D or 3D structures all face various…

Computer Vision and Pattern Recognition · Computer Science 2026-02-10 Tianyu Sun , Zhoujie Fu , Bang Zhang , Guosheng Lin

Lighting plays a pivotal role in ensuring the naturalness and aesthetic quality of video generation. However, the impact of lighting is deeply coupled with other factors of videos, e.g., objects and scenes. Thus, it remains challenging to…

Computer Vision and Pattern Recognition · Computer Science 2025-08-26 Yuxin Zhang , Dandan Zheng , Biao Gong , Shiwen Wang , Jingdong Chen , Ming Yang , Weiming Dong , Changsheng Xu