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Recent progress in diffusion models has significantly advanced the field of human image animation. While existing methods can generate temporally consistent results for short or regular motions, significant challenges remain, particularly…

Computer Vision and Pattern Recognition · Computer Science 2025-12-29 Shen Zheng , Jiaran Cai , Yuansheng Guan , Shenneng Huang , Xingpei Ma , Junjie Cao , Hanfeng Zhao , Qiang Zhang , Shunsi Zhang , Xiao-Ping Zhang

Portrait animation aims to generate photo-realistic videos from a single source image by reenacting the expression and pose from a driving video. While early methods relied on 3D morphable models or feature warping techniques, they often…

Computer Vision and Pattern Recognition · Computer Science 2025-09-23 Mallikarjun B. R. , Fei Yin , Vikram Voleti , Nikita Drobyshev , Maksim Lapin , Aaryaman Vasishta , Varun Jampani

Recent advances in 4D generation mainly focus on generating 4D content by distilling pre-trained text or single-view image-conditioned models. It is inconvenient for them to take advantage of various off-the-shelf 3D assets with multi-view…

Computer Vision and Pattern Recognition · Computer Science 2024-09-10 Yanqin Jiang , Chaohui Yu , Chenjie Cao , Fan Wang , Weiming Hu , Jin Gao

The rapid development of diffusion models has significantly advanced AI-generated content (AIGC), particularly in Text-to-Image (T2I) and Text-to-Video (T2V) generation. Text-based video editing, leveraging these generative capabilities,…

Computer Vision and Pattern Recognition · Computer Science 2025-01-22 Yupeng Chen , Penglin Chen , Xiaoyu Zhang , Yixian Huang , Qian Xie

Long video generation remains a challenging and compelling topic in computer vision. Diffusion based models, among the various approaches to video generation, have achieved state of the art quality with their iterative denoising procedures.…

Computer Vision and Pattern Recognition · Computer Science 2025-03-14 Siyang Zhang , Harry Yang , Ser-Nam Lim

The text-guided video inpainting technique has significantly improved the performance of content generation applications. A recent family for these improvements uses diffusion models, which have become essential for achieving high-quality…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Bohai Gu , Hao Luo , Song Guo , Peiran Dong , Qihua Zhou

Diffusion models have achieved remarkable success in image generation, with applications broadening across various domains. Inpainting is one such application that can benefit significantly from diffusion models. Existing methods either…

Computer Vision and Pattern Recognition · Computer Science 2026-02-06 Sora Kim , Sungho Suh , Minsik Lee

Diffusion models have demonstrated remarkable capabilities in text-to-image and text-to-video generation, opening up possibilities for video editing based on textual input. However, the computational cost associated with sequential sampling…

Computer Vision and Pattern Recognition · Computer Science 2024-11-20 Youyuan Zhang , Xuan Ju , James J. Clark

Text-guided diffusion models have revolutionized image generation and editing, offering exceptional realism and diversity. Specifically, in the context of diffusion-based editing, where a source image is edited according to a target prompt,…

Computer Vision and Pattern Recognition · Computer Science 2023-10-20 Xuan Ju , Ailing Zeng , Yuxuan Bian , Shaoteng Liu , Qiang Xu

Research on video generation has recently made tremendous progress, enabling high-quality videos to be generated from text prompts or images. Adding control to the video generation process is an important goal moving forward and recent…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Zhengfei Kuang , Shengqu Cai , Hao He , Yinghao Xu , Hongsheng Li , Leonidas Guibas , Gordon Wetzstein

Recent advances in diffusion models have revolutionized video generation, offering superior temporal consistency and visual quality compared to traditional generative adversarial networks-based approaches. While this emerging field shows…

Computer Vision and Pattern Recognition · Computer Science 2026-02-11 Yimu Wang , Xuye Liu , Wei Pang , Li Ma , Shuai Yuan , Paul Debevec , Ning Yu

While current research predominantly focuses on image-based colorization, the domain of video-based colorization remains relatively unexplored. Most existing video colorization techniques operate on a frame-by-frame basis, often overlooking…

Computer Vision and Pattern Recognition · Computer Science 2024-05-10 Rory Ward , Dan Bigioi , Shubhajit Basak , John G. Breslin , Peter Corcoran

We introduce Follow-Your-Creation, a novel 4D video creation framework capable of both generating and editing 4D content from a single monocular video input. By leveraging a powerful video inpainting foundation model as a generative prior,…

Computer Vision and Pattern Recognition · Computer Science 2025-06-06 Yue Ma , Kunyu Feng , Xinhua Zhang , Hongyu Liu , David Junhao Zhang , Jinbo Xing , Yinhan Zhang , Ayden Yang , Zeyu Wang , Qifeng Chen

Reconstructing complete and animatable 3D human avatars from monocular videos remains challenging, particularly under severe occlusions. While 3D Gaussian Splatting has enabled photorealistic human rendering, existing methods struggle with…

Computer Vision and Pattern Recognition · Computer Science 2026-01-06 Jinlong Fan , Shanshan Zhao , Liang Zheng , Jing Zhang , Yuxiang Yang , Mingming Gong

Video inpainting aims to fill spatio-temporal "corrupted" regions with plausible content. To achieve this goal, it is necessary to find correspondences from neighbouring frames to faithfully hallucinate the unknown content. Current methods…

Computer Vision and Pattern Recognition · Computer Science 2021-04-09 Xueyan Zou , Linjie Yang , Ding Liu , Yong Jae Lee

Diffusion models have emerged as highly effective techniques for inpainting, however, they remain constrained by slow sampling rates. While recent advances have enhanced generation quality, they have also increased sampling time, thereby…

Computer Vision and Pattern Recognition · Computer Science 2025-06-25 Tsiry Mayet , Pourya Shamsolmoali , Simon Bernard , Eric Granger , Romain Hérault , Clement Chatelain

In-context image editing aims to modify images based on a contextual sequence comprising text and previously generated images. Existing methods typically depend on task-specific pipelines and expert models (e.g., segmentation and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Leigang Qu , Feng Cheng , Ziyan Yang , Qi Zhao , Shanchuan Lin , Yichun Shi , Yicong Li , Wenjie Wang , Tat-Seng Chua , Lu Jiang

Image inpainting is the task of reconstructing missing or damaged parts of an image in a way that seamlessly blends with the surrounding content. With the advent of advanced generative models, especially diffusion models and generative…

Computer Vision and Pattern Recognition · Computer Science 2025-06-17 Xingzhong Hou , Jie Wu , Boxiao Liu , Yi Zhang , Guanglu Song , Yunpeng Liu , Yu Liu , Haihang You

AI-generated content has attracted lots of attention recently, but photo-realistic video synthesis is still challenging. Although many attempts using GANs and autoregressive models have been made in this area, the visual quality and length…

Computer Vision and Pattern Recognition · Computer Science 2023-03-21 Yingqing He , Tianyu Yang , Yong Zhang , Ying Shan , Qifeng Chen

Editing videos with textual guidance has garnered popularity due to its streamlined process which mandates users to solely edit the text prompt corresponding to the source video. Recent studies have explored and exploited large-scale…

Computer Vision and Pattern Recognition · Computer Science 2024-12-17 Yuanzhi Wang , Yong Li , Mengyi Liu , Xiaoya Zhang , Xin Liu , Zhen Cui , Antoni B. Chan