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We present PoseDiff, a conditional diffusion model that unifies robot state estimation and control within a single framework. At its core, PoseDiff maps raw visual observations into structured robot states-such as 3D keypoints or joint…

Robotics · Computer Science 2025-11-03 Haozhuo Zhang , Michele Caprio , Jing Shao , Qiang Zhang , Jian Tang , Shanghang Zhang , Wei Pan

Zero-shot customized video generation has gained significant attention due to its substantial application potential. Existing methods rely on additional models to extract and inject reference subject features, assuming that the Video…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Tao Wu , Yong Zhang , Xiaodong Cun , Zhongang Qi , Junfu Pu , Huanzhang Dou , Guangcong Zheng , Ying Shan , Xi Li

Despite tremendous recent progress in human video generation, generative video diffusion models still struggle to capture the dynamics and physics of human motions faithfully. In this paper, we propose a new framework for human video…

Computer Vision and Pattern Recognition · Computer Science 2026-04-08 Tao Hu , Varun Jampani

Diffusion models are emerging expressive generative models, in which a large number of time steps (inference steps) are required for a single image generation. To accelerate such tedious process, reducing steps uniformly is considered as an…

Computer Vision and Pattern Recognition · Computer Science 2023-09-26 Lijiang Li , Huixia Li , Xiawu Zheng , Jie Wu , Xuefeng Xiao , Rui Wang , Min Zheng , Xin Pan , Fei Chao , Rongrong Ji

For recent diffusion-based generative models, maintaining consistent content across a series of generated images, especially those containing subjects and complex details, presents a significant challenge. In this paper, we propose a new…

Computer Vision and Pattern Recognition · Computer Science 2024-05-03 Yupeng Zhou , Daquan Zhou , Ming-Ming Cheng , Jiashi Feng , Qibin Hou

Video editing is a challenging task that requires manipulating videos on both the spatial and temporal dimensions. Existing methods for video editing mainly focus on changing the appearance or style of the objects in the video, while…

Computer Vision and Pattern Recognition · Computer Science 2023-12-06 Yao Teng , Enze Xie , Yue Wu , Haoyu Han , Zhenguo Li , Xihui Liu

Diffusion-based human animation aims to animate a human character based on a source human image as well as driving signals such as a sequence of poses. Leveraging the generative capacity of diffusion model, existing approaches are able to…

Computer Vision and Pattern Recognition · Computer Science 2024-12-30 Fa-Ting Hong , Zhan Xu , Haiyang Liu , Qinjie Lin , Luchuan Song , Zhixin Shu , Yang Zhou , Duygu Ceylan , Dan Xu

The body movements accompanying speech aid speakers in expressing their ideas. Co-speech motion generation is one of the important approaches for synthesizing realistic avatars. Due to the intricate correspondence between speech and motion,…

Multimedia · Computer Science 2024-08-28 Sen Wang , Jiangning Zhang , Xin Tan , Zhifeng Xie , Chengjie Wang , Lizhuang Ma

Generating multiview images from a single view facilitates the rapid generation of a 3D mesh conditioned on a single image. Recent methods that introduce 3D global representation into diffusion models have shown the potential to generate…

Computer Vision and Pattern Recognition · Computer Science 2024-04-03 Zehuan Huang , Hao Wen , Junting Dong , Yaohui Wang , Yangguang Li , Xinyuan Chen , Yan-Pei Cao , Ding Liang , Yu Qiao , Bo Dai , Lu Sheng

Existing multi-modal image fusion methods fail to address the compound degradations presented in source images, resulting in fusion images plagued by noise, color bias, improper exposure, \textit{etc}. Additionally, these methods often…

Computer Vision and Pattern Recognition · Computer Science 2024-11-01 Hao Zhang , Lei Cao , Jiayi Ma

Current discriminative depth estimation methods often produce blurry artifacts, while generative approaches suffer from slow sampling due to curvatures in the noise-to-depth transport. Our method addresses these challenges by framing depth…

Computer Vision and Pattern Recognition · Computer Science 2024-12-20 Ming Gui , Johannes Schusterbauer , Ulrich Prestel , Pingchuan Ma , Dmytro Kotovenko , Olga Grebenkova , Stefan Andreas Baumann , Vincent Tao Hu , Björn Ommer

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

We propose a generative model that, given a coarsely edited image, synthesizes a photorealistic output that follows the prescribed layout. Our method transfers fine details from the original image and preserve the identity of its parts.…

Computer Vision and Pattern Recognition · Computer Science 2025-08-08 Hadi Alzayer , Zhihao Xia , Xuaner Zhang , Eli Shechtman , Jia-Bin Huang , Michael Gharbi

Text-based 3D human motion editing is a critical yet challenging task in computer vision and graphics. While training-free approaches have been explored, the recent release of the MotionFix dataset, which includes source-text-motion…

Computer Vision and Pattern Recognition · Computer Science 2025-03-27 Zhengyuan Li , Kai Cheng , Anindita Ghosh , Uttaran Bhattacharya , Liangyan Gui , Aniket Bera

This paper explores image editing under the joint control of text and drag interactions. While recent advances in text-driven and drag-driven editing have achieved remarkable progress, they suffer from complementary limitations: text-driven…

Computer Vision and Pattern Recognition · Computer Science 2025-09-29 Qihang Wang , Yaxiong Wang , Lechao Cheng , Zhun Zhong

Motion segmentation from a single moving camera presents a significant challenge in the field of computer vision. This challenge is compounded by the unknown camera movements and the lack of depth information of the scene. While deep…

Computer Vision and Pattern Recognition · Computer Science 2024-06-28 Yuxiang Huang , Yuhao Chen , John Zelek

Recent advancements in diffusion models have significantly improved video generation and editing capabilities. However, multi-grained video editing, which encompasses class-level, instance-level, and part-level modifications, remains a…

Computer Vision and Pattern Recognition · Computer Science 2025-02-25 Xiangpeng Yang , Linchao Zhu , Hehe Fan , Yi Yang

Soccer is a globally renowned sport with significant applications in video games and VR/AR. However, generating realistic soccer motions remains challenging due to the intricate interactions between the human player and the ball. In this…

Computer Vision and Pattern Recognition · Computer Science 2024-11-26 Hongdi Yang , Chengyang Li , Zhenxuan Wu , Gaozheng Li , Jingya Wang , Jingyi Yu , Zhuo Su , Lan Xu

Interactive image editing allows users to modify images through visual interaction operations such as drawing, clicking, and dragging. Existing methods construct such supervision signals from videos, as they capture how objects change with…

Computer Vision and Pattern Recognition · Computer Science 2025-01-15 Yabo Zhang , Xinpeng Zhou , Yihan Zeng , Hang Xu , Hui Li , Wangmeng Zuo

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
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