English
Related papers

Related papers: ViMo: Generating Motions from Casual Videos

200 papers

In this paper, we investigate the generation of new video backgrounds given a human foreground video, a camera pose, and a reference scene image. This task presents three key challenges. First, the generated background should precisely…

Text-to-video diffusion models have advanced video generation significantly. However, customizing these models to generate videos with tailored motions presents a substantial challenge. In specific, they encounter hurdles in (a) accurately…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Hyeonho Jeong , Geon Yeong Park , Jong Chul Ye

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

Co-speech gestures, if presented in the lively form of videos, can achieve superior visual effects in human-machine interaction. While previous works mostly generate structural human skeletons, resulting in the omission of appearance…

Computer Vision and Pattern Recognition · Computer Science 2024-04-03 Xu He , Qiaochu Huang , Zhensong Zhang , Zhiwei Lin , Zhiyong Wu , Sicheng Yang , Minglei Li , Zhiyi Chen , Songcen Xu , Xiaofei Wu

Text-to-motion generation, which translates textual descriptions into human motions, faces the challenge that users often struggle to precisely convey their intended motions through text alone. To address this issue, this paper introduces…

Computer Vision and Pattern Recognition · Computer Science 2026-05-21 Tao Wang , Lei Jin , Zhihua Wu , Qiaozhi He , Jiaming Chu , Yu Cheng , Junliang Xing , Jian Zhao , Shuicheng Yan , Li Wang

Video generation remains a challenging task due to spatiotemporal complexity and the requirement of synthesizing diverse motions with temporal consistency. Previous works attempt to generate videos in arbitrary lengths either in an…

Computer Vision and Pattern Recognition · Computer Science 2023-04-07 Xiaoqian Shen , Xiang Li , Mohamed Elhoseiny

Despite rapid advances in video generative models, robust metrics for evaluating visual and temporal correctness of complex human actions remain elusive. Critically, existing pure-vision encoders and Multimodal Large Language Models (MLLMs)…

Computer Vision and Pattern Recognition · Computer Science 2025-12-04 Xavier Thomas , Youngsun Lim , Ananya Srinivasan , Audrey Zheng , Deepti Ghadiyaram

Large-scale video generative models, capable of creating realistic videos of diverse visual concepts, are strong candidates for general-purpose physical world simulators. However, their adherence to physical commonsense across real-world…

Computer Vision and Pattern Recognition · Computer Science 2025-03-11 Hritik Bansal , Clark Peng , Yonatan Bitton , Roman Goldenberg , Aditya Grover , Kai-Wei Chang

Recent diffusion methods have made significant progress in generating videos from single images due to their powerful visual generation capabilities. However, challenges persist in image-to-video synthesis, particularly in human video…

Computer Vision and Pattern Recognition · Computer Science 2026-02-25 Tiantian Wang , Chun-Han Yao , Tao Hu , Mallikarjun Byrasandra Ramalinga Reddy , Ming-Hsuan Yang , Varun Jampani

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

Significant progress has been made in text-to-video generation through the use of powerful generative models and large-scale internet data. However, substantial challenges remain in precisely controlling individual concepts within the…

Computer Vision and Pattern Recognition · Computer Science 2024-09-30 Hanxin Zhu , Tianyu He , Anni Tang , Junliang Guo , Zhibo Chen , Jiang Bian

World models, which predict future transitions from past observation and action sequences, have shown great promise for improving data efficiency in sequential decision-making. However, existing world models often require extensive…

Computer Vision and Pattern Recognition · Computer Science 2026-03-10 Siqiao Huang , Jialong Wu , Qixing Zhou , Shangchen Miao , Mingsheng Long

Recent advances in internet-scale video data pretraining have led to the development of text-to-video generative models that can create high-quality videos across a broad range of visual concepts, synthesize realistic motions and render…

Computer Vision and Pattern Recognition · Computer Science 2024-10-04 Hritik Bansal , Zongyu Lin , Tianyi Xie , Zeshun Zong , Michal Yarom , Yonatan Bitton , Chenfanfu Jiang , Yizhou Sun , Kai-Wei Chang , Aditya Grover

Self-captured full-body videos are popular, but most deployments require mounted cameras, carefully-framed shots, and repeated practice. We propose a more convenient solution that enables full-body video capture using handheld mobile…

Computer Vision and Pattern Recognition · Computer Science 2025-12-02 Bowei Chen , Brian Curless , Ira Kemelmacher-Shlizerman , Steven M. Seitz

Building on the momentum of image generation diffusion models, there is an increasing interest in video-based diffusion models. However, video generation poses greater challenges due to its higher-dimensional nature, the scarcity of…

Computer Vision and Pattern Recognition · Computer Science 2024-11-01 Aimon Rahman , Malsha V. Perera , Vishal M. Patel

Controllability, temporal coherence, and detail synthesis remain the most critical challenges in video generation. In this paper, we focus on a commonly used yet underexplored cinematic technique known as Frame In and Frame Out.…

Computer Vision and Pattern Recognition · Computer Science 2025-10-27 Boyang Wang , Xuweiyi Chen , Matheus Gadelha , Zezhou Cheng

Panoramic image stitching provides a unified, wide-angle view of a scene that extends beyond the camera's field of view. Stitching frames of a panning video into a panoramic photograph is a well-understood problem for stationary scenes, but…

Computer Vision and Pattern Recognition · Computer Science 2024-10-29 Jingwei Ma , Erika Lu , Roni Paiss , Shiran Zada , Aleksander Holynski , Tali Dekel , Brian Curless , Michael Rubinstein , Forrester Cole

We revisit human motion synthesis, a task useful in various real world applications, in this paper. Whereas a number of methods have been developed previously for this task, they are often limited in two aspects: focusing on the poses while…

Computer Vision and Pattern Recognition · Computer Science 2021-06-01 Jingbo Wang , Sijie Yan , Bo Dai , Dahua LIn

Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e.g., increasing the frame rate of the more dynamic portion of the video…

Computer Vision and Pattern Recognition · Computer Science 2021-03-31 Sunghyun Park , Kangyeol Kim , Junsoo Lee , Jaegul Choo , Joonseok Lee , Sookyung Kim , Edward Choi

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