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This work presents CineTransfer, an algorithmic framework that drives a robot to record a video sequence that mimics the cinematographic style of an input video. We propose features that abstract the aesthetic style of the input video, so…

Robotics · Computer Science 2023-10-09 Pablo Pueyo , Eduardo Montijano , Ana C. Murillo , Mac Schwager

Novel view synthesis from a single image has recently attracted a lot of attention, and it has been primarily advanced by 3D deep learning and rendering techniques. However, most work is still limited by synthesizing new views within…

Computer Vision and Pattern Recognition · Computer Science 2022-03-18 Xuanchi Ren , Xiaolong Wang

Generated video scenes for action-centric sequence descriptions, such as recipe instructions and do-it-yourself projects, often include non-linear patterns, where the next video may need to be visually consistent not with the immediately…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Vasco Ramos , Yonatan Bitton , Michal Yarom , Idan Szpektor , Joao Magalhaes

Point tracking aims to follow visual points through complex motion, occlusion, and viewpoint changes, and has advanced rapidly with modern foundation models. Yet progress toward general point tracking remains constrained by limited…

Computer Vision and Pattern Recognition · Computer Science 2026-02-05 Weiguang Zhao , Haoran Xu , Xingyu Miao , Qin Zhao , Rui Zhang , Kaizhu Huang , Ning Gao , Peizhou Cao , Mingze Sun , Mulin Yu , Tao Lu , Linning Xu , Junting Dong , Jiangmiao Pang

Text-driven video generation has democratized film creation, but camera control in cinematic multi-shot scenarios remains a significant block. Implicit textual prompts lack precision, while explicit trajectory conditioning imposes…

Computer Vision and Pattern Recognition · Computer Science 2026-03-13 Songlin Yang , Zhe Wang , Xuyi Yang , Songchun Zhang , Xianghao Kong , Taiyi Wu , Xiaotong Zhao , Ran Zhang , Alan Zhao , Anyi Rao

Video generation has witnessed remarkable progress with the advent of deep generative models, particularly diffusion models. While existing methods excel in generating high-quality videos from text prompts or single images, personalized…

Computer Vision and Pattern Recognition · Computer Science 2025-03-14 Yufan Deng , Xun Guo , Yizhi Wang , Jacob Zhiyuan Fang , Angtian Wang , Shenghai Yuan , Yiding Yang , Bo Liu , Haibin Huang , Chongyang Ma

We present 3DScenePrompt, a framework that generates the next video chunk from arbitrary-length input while enabling precise camera control and preserving scene consistency. Unlike methods conditioned on a single image or a short clip, we…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 JoungBin Lee , Jaewoo Jung , Jisang Han , Takuya Narihira , Kazumi Fukuda , Junyoung Seo , Sunghwan Hong , Yuki Mitsufuji , Seungryong Kim

Robust scene segmentation and keyframe extraction are essential preprocessing steps in video understanding pipelines, supporting tasks such as indexing, summarization, and semantic retrieval. However, existing methods often lack…

Computer Vision and Pattern Recognition · Computer Science 2025-06-03 Vasilii Korolkov

Recent progress in image and video synthesis has inspired their use in advancing 3D scene generation. However, we observe that text-to-image and -video approaches struggle to maintain scene- and object-level consistency beyond a limited…

Computer Vision and Pattern Recognition · Computer Science 2026-03-26 Manuel-Andreas Schneider , Angela Dai

Recent advancements in character video synthesis still depend on extensive fine-tuning or complex 3D modeling processes, which can restrict accessibility and hinder real-time applicability. To address these challenges, we propose a simple…

Computer Vision and Pattern Recognition · Computer Science 2025-01-14 Di Qiu , Zheng Chen , Rui Wang , Mingyuan Fan , Changqian Yu , Junshi Huang , Xiang Wen

Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In these media, dynamic and still elements are juxtaposed to create an artistic and narrative experience. Creating a high-quality, aesthetically pleasing cinemagraph…

Computer Vision and Pattern Recognition · Computer Science 2017-08-11 Tae-Hyun Oh , Kyungdon Joo , Neel Joshi , Baoyuan Wang , In So Kweon , Sing Bing Kang

Synthesizing interactive 3D scenes from text is essential for gaming, virtual reality, and embodied AI. However, existing methods face several challenges. Learning-based approaches depend on small-scale indoor datasets, limiting the scene…

Computer Vision and Pattern Recognition · Computer Science 2025-05-06 Lu Ling , Chen-Hsuan Lin , Tsung-Yi Lin , Yifan Ding , Yu Zeng , Yichen Sheng , Yunhao Ge , Ming-Yu Liu , Aniket Bera , Zhaoshuo Li

Current one-pass 3D scene synthesis methods often suffer from spatial hallucinations, such as collisions, due to a lack of deliberative reasoning. To bridge this gap, we introduce SceneReVis, a vision-grounded self-reflection framework that…

Computer Vision and Pattern Recognition · Computer Science 2026-02-11 Yang Zhao , Shizhao Sun , Meisheng Zhang , Yingdong Shi , Xubo Yang , Jiang Bian

Human video synthesis aims to create lifelike characters in various environments, with wide applications in VR, storytelling, and content creation. While 2D diffusion-based methods have made significant progress, they struggle to generalize…

Computer Vision and Pattern Recognition · Computer Science 2024-12-19 Liyuan Cui , Xiaogang Xu , Wenqi Dong , Zesong Yang , Hujun Bao , Zhaopeng Cui

In this paper, we study video synthesis with emphasis on simplifying the generation conditions. Most existing video synthesis models or datasets are designed to address complex motions of a single object, lacking the ability of…

Computer Vision and Pattern Recognition · Computer Science 2023-05-24 Yang Wu , Zhibin Liu , Hefeng Wu , Liang Lin

Generating long videos that can show complex stories, like movie scenes from scripts, has great promise and offers much more than short clips. However, current methods that use autoregression with diffusion models often struggle because…

Computer Vision and Pattern Recognition · Computer Science 2025-05-28 Guangcong Zheng , Jianlong Yuan , Bo Wang , Haoyang Huang , Guoqing Ma , Nan Duan

Synthetic 3D scenes are essential for developing Physical AI and generative models. Existing procedural generation methods often have low output throughput, creating a significant bottleneck in scaling up dataset creation. In this work, we…

Robotics · Computer Science 2025-12-19 Jinghuan Shang , Harsh Patel , Ran Gong , Karl Schmeckpeper

Interactive video generation has significant potential for scene simulation and video creation. However, existing methods often struggle with maintaining scene consistency during long video generation under dynamic camera control due to…

Computer Vision and Pattern Recognition · Computer Science 2026-03-30 Xinhang Gao , Junlin Guan , Shuhan Luo , Wenzhuo Li , Guanghuan Tan , Jiacheng Wang

Scene text synthesis involves rendering specified texts onto arbitrary images. Current methods typically formulate this task in an end-to-end manner but lack effective character-level guidance during training. Besides, their text encoders,…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Yibin Wang , Weizhong Zhang , Honghui Xu , Cheng Jin

We propose SceneTex, a novel method for effectively generating high-quality and style-consistent textures for indoor scenes using depth-to-image diffusion priors. Unlike previous methods that either iteratively warp 2D views onto a mesh…

Computer Vision and Pattern Recognition · Computer Science 2023-11-30 Dave Zhenyu Chen , Haoxuan Li , Hsin-Ying Lee , Sergey Tulyakov , Matthias Nießner