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We propose GoalFlow, an end-to-end autonomous driving method for generating high-quality multimodal trajectories. In autonomous driving scenarios, there is rarely a single suitable trajectory. Recent methods have increasingly focused on…

Computer Vision and Pattern Recognition · Computer Science 2025-10-02 Zebin Xing , Xingyu Zhang , Yang Hu , Bo Jiang , Tong He , Qian Zhang , Xiaoxiao Long , Wei Yin

Video and audio are closely correlated modalities that humans naturally perceive together. While recent advancements have enabled the generation of audio or video from text, producing both modalities simultaneously still typically relies on…

The essence of a video lies in its dynamic motions, including character actions, object movements, and camera movements. While text-to-video generative diffusion models have recently advanced in creating diverse contents, controlling…

Computer Vision and Pattern Recognition · Computer Science 2024-01-04 Yuxin Zhang , Fan Tang , Nisha Huang , Haibin Huang , Chongyang Ma , Weiming Dong , Changsheng Xu

With the rapid advancement of diffusion-based generative models, portrait image animation has achieved remarkable results. However, it still faces challenges in temporally consistent video generation and fast sampling due to its iterative…

Computer Vision and Pattern Recognition · Computer Science 2025-09-22 Taekyung Ki , Dongchan Min , Gyeongsu Chae

Large-scale pre-trained diffusion models have exhibited remarkable capabilities in diverse video generations. Given a set of video clips of the same motion concept, the task of Motion Customization is to adapt existing text-to-video…

Computer Vision and Pattern Recognition · Computer Science 2023-10-13 Rui Zhao , Yuchao Gu , Jay Zhangjie Wu , David Junhao Zhang , Jiawei Liu , Weijia Wu , Jussi Keppo , Mike Zheng Shou

Motion generation is essential for animating virtual characters and embodied agents. While recent text-driven methods have made significant strides, they often struggle with achieving precise alignment between linguistic descriptions and…

Computer Vision and Pattern Recognition · Computer Science 2025-08-28 Zhiting Gao , Dan Song , Diqiong Jiang , Chao Xue , An-An Liu

Video generation models trained on heterogeneous data with likelihood-surrogate objectives can produce visually plausible rollouts that violate physical constraints in embodied manipulation. Although reinforcement-learning post-training…

Computer Vision and Pattern Recognition · Computer Science 2026-05-15 Zhenyang Ni , Yijiang Li , Ruochen Jiao , Simon Sinong Zhan , Sipeng Chen , Zhenfei Yin , Minshuo Chen , Philip Torr , Zhaoran Wang , Qi Zhu

We introduce OneDiffusion, a versatile, large-scale diffusion model that seamlessly supports bidirectional image synthesis and understanding across diverse tasks. It enables conditional generation from inputs such as text, depth, pose,…

Computer Vision and Pattern Recognition · Computer Science 2025-06-16 Duong H. Le , Tuan Pham , Sangho Lee , Christopher Clark , Aniruddha Kembhavi , Stephan Mandt , Ranjay Krishna , Jiasen Lu

In ophthalmic surgery, developing an AI system capable of interpreting surgical videos and predicting subsequent operations requires numerous ophthalmic surgical videos with high-quality annotations, which are difficult to collect due to…

Image and Video Processing · Electrical Eng. & Systems 2025-07-15 Wei Li , Ming Hu , Guoan Wang , Lihao Liu , Kaijing Zhou , Junzhi Ning , Xin Guo , Zongyuan Ge , Lixu Gu , Junjun He

Existing pretrained text-to-video (T2V) models have demonstrated impressive abilities in generating realistic videos with basic motion or camera movement. However, these models exhibit significant limitations when generating intricate,…

Computer Vision and Pattern Recognition · Computer Science 2024-12-03 Xiaomin Li , Xu Jia , Qinghe Wang , Haiwen Diao , Mengmeng Ge , Pengxiang Li , You He , Huchuan Lu

Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation across various tasks. Yet, most existing approaches remain limited in handling multiple types of…

Robotics · Computer Science 2026-01-15 Zewen Yang , Xiaobing Dai , Dian Yu , Zhijun Li , Majid Khadiv , Sandra Hirche , Sami Haddadin

We introduce a novel diffusion-based video generation method, generating a video showing multiple events given multiple individual sentences from the user. Our method does not require a large-scale video dataset since our method uses a…

Computer Vision and Pattern Recognition · Computer Science 2024-07-17 Gyeongrok Oh , Jaehwan Jeong , Sieun Kim , Wonmin Byeon , Jinkyu Kim , Sungwoong Kim , Sangpil Kim

Despite remarkable achievements in video synthesis, achieving granular control over complex dynamics, such as nuanced movement among multiple interacting objects, still presents a significant hurdle for dynamic world modeling, compounded by…

Computer Vision and Pattern Recognition · Computer Science 2024-03-21 Pengxiang Li , Kai Chen , Zhili Liu , Ruiyuan Gao , Lanqing Hong , Guo Zhou , Hua Yao , Dit-Yan Yeung , Huchuan Lu , Xu Jia

A large amount of procedural videos on the web show how to complete various tasks. These tasks can often be accomplished in different ways and step orderings, with some steps able to be performed simultaneously, while others are constrained…

Computer Vision and Pattern Recognition · Computer Science 2024-09-04 Jiatong Li , Kalliopi Basioti , Vladimir Pavlovic

Notable breakthroughs in unified understanding and generation modeling have led to remarkable advancements in image understanding, reasoning, production and editing, yet current foundational models predominantly focus on processing images,…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Zhiyu Tan , Hao Yang , Luozheng Qin , Jia Gong , Mengping Yang , Hao Li

A recent paper by Gatys et al. describes a method for rendering an image in the style of another image. First, they use convolutional neural network features to build a statistical model for the style of an image. Then they create a new…

Computer Vision and Pattern Recognition · Computer Science 2016-05-27 Alexander G. Anderson , Cory P. Berg , Daniel P. Mossing , Bruno A. Olshausen

Generating long, high-quality videos remains a challenge due to the complex interplay of spatial and temporal dynamics and hardware limitations. In this work, we introduce MaskFlow, a unified video generation framework that combines…

Computer Vision and Pattern Recognition · Computer Science 2025-03-13 Michael Fuest , Vincent Tao Hu , Björn Ommer

Text-to-motion generation has gained increasing attention, but most existing methods are limited to generating short-term motions that correspond to a single sentence describing a single action. However, when a text stream describes a…

Computer Vision and Pattern Recognition · Computer Science 2023-08-04 Zhao Yang , Bing Su , Ji-Rong Wen

Generating sound effects for videos often requires creating artistic sound effects that diverge significantly from real-life sources and flexible control in the sound design. To address this problem, we introduce MultiFoley, a model…

Computer Vision and Pattern Recognition · Computer Science 2025-03-18 Ziyang Chen , Prem Seetharaman , Bryan Russell , Oriol Nieto , David Bourgin , Andrew Owens , Justin Salamon

This paper introduces DynaFlow, a novel framework that embeds a differentiable simulator directly into a flow matching model. By generating trajectories in the action space and mapping them to dynamically feasible state trajectories via the…

Robotics · Computer Science 2026-03-17 Sowoo Lee , Dongyun Kang , Jaehyun Park , Hae-Won Park
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