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In e-commerce and digital marketing, generating high-fidelity human-product demonstration videos is important for effective product presentation. However, most existing frameworks either fail to preserve the identities of both humans and…

Computer Vision and Pattern Recognition · Computer Science 2025-08-28 Lizhen Wang , Zhurong Xia , Tianshu Hu , Pengrui Wang , Pengfei Wei , Zerong Zheng , Ming Zhou , Yuan Zhang , Mingyuan Gao

Despite the remarkable process of talking-head-based avatar-creating solutions, directly generating anchor-style videos with full-body motions remains challenging. In this study, we propose Make-Your-Anchor, a novel system necessitating…

Computer Vision and Pattern Recognition · Computer Science 2024-03-26 Ziyao Huang , Fan Tang , Yong Zhang , Xiaodong Cun , Juan Cao , Jintao Li , Tong-Yee Lee

Despite significant advancements in customizing text-to-image and video generation models, generating images and videos that effectively integrate multiple personalized concepts remains a challenging task. To address this, we present…

Computer Vision and Pattern Recognition · Computer Science 2025-03-05 Gihyun Kwon , Jong Chul Ye

Large-scale, pre-trained Text-to-Image (T2I) diffusion models have gained significant popularity in image generation tasks and have shown unexpected potential in image Super-Resolution (SR). However, most existing T2I diffusion models are…

Computer Vision and Pattern Recognition · Computer Science 2024-11-20 Brian B. Moser , Stanislav Frolov , Tobias C. Nauen , Federico Raue , Andreas Dengel

Video generation has achieved remarkable progress with the introduction of diffusion models, which have significantly improved the quality of generated videos. However, recent research has primarily focused on scaling up model training,…

Computer Vision and Pattern Recognition · Computer Science 2025-01-16 Chenyang Si , Weichen Fan , Zhengyao Lv , Ziqi Huang , Yu Qiao , Ziwei Liu

Denoising-based diffusion transformers, despite their strong generation performance, suffer from inefficient training convergence. Existing methods addressing this issue, such as REPA (relying on external representation encoders) or SRA…

Computer Vision and Pattern Recognition · Computer Science 2026-03-09 Mengmeng Wang , Dengyang Jiang , Liuzhuozheng Li , Yucheng Lin , Guojiang Shen , Xiangjie Kong , Yong Liu , Guang Dai , Jingdong Wang

We introduce an approach for augmenting text-to-video generation models with customized motions, extending their capabilities beyond the motions depicted in the original training data. By leveraging a few video samples demonstrating…

Computer Vision and Pattern Recognition · Computer Science 2024-12-11 Joanna Materzynska , Josef Sivic , Eli Shechtman , Antonio Torralba , Richard Zhang , Bryan Russell

Several text-to-video diffusion models have demonstrated commendable capabilities in synthesizing high-quality video content. However, it remains a formidable challenge pertaining to maintaining temporal consistency and ensuring action…

Computer Vision and Pattern Recognition · Computer Science 2024-03-14 Deshun Yang , Luhui Hu , Yu Tian , Zihao Li , Chris Kelly , Bang Yang , Cindy Yang , Yuexian Zou

Multi-object video motion transfer poses significant challenges for Diffusion Transformer (DiT) architectures due to inherent motion entanglement and lack of object-level control. We present MultiMotion, a novel unified framework that…

Computer Vision and Pattern Recognition · Computer Science 2025-12-15 Penghui Liu , Jiangshan Wang , Yutong Shen , Shanhui Mo , Chenyang Qi , Yue Ma

Multi-person interactive motion generation, a critical yet under-explored domain in computer character animation, poses significant challenges such as intricate modeling of inter-human interactions beyond individual motions and generating…

Computer Vision and Pattern Recognition · Computer Science 2024-12-24 Boyuan Li , Xihua Wang , Ruihua Song , Wenbing Huang

Achieving semantic alignment across diverse video generation conditions remains a significant challenge. Methods that rely on explicit structural guidance often enforce rigid spatial constraints that limit semantic flexibility, whereas…

Computer Vision and Pattern Recognition · Computer Science 2026-04-02 Zexi Wu , Baolu Li , Jing Dai , Yiming Zhang , Yue Ma , Qinghe Wang , Xu Jia , Hongming Xu

Generating realistic human-human interactions is a challenging task that requires not only high-quality individual body and hand motions, but also coherent coordination among all interactants. Due to limitations in available data and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-30 Pablo Ruiz-Ponce , Sergio Escalera , José García-Rodríguez , Jiankang Deng , Rolandos Alexandros Potamias

Creating editable videos that depict complex interactions between multiple objects in various artistic styles has long been a challenging task in filmmaking. Progress is often hampered by the scarcity of data sets that contain paired text…

Computer Vision and Pattern Recognition · Computer Science 2024-08-30 Anisha Jain

Text-to-video (T2V) generation technology holds potential to transform multiple domains such as education, marketing, entertainment, and assistive technologies for individuals with visual or reading comprehension challenges, by creating…

Graphics · Computer Science 2025-10-07 Nilay Kumar , Priyansh Bhandari , G. Maragatham

Video diffusion transformers (vDiTs) have made tremendous progress in text-to-video generation, but their high compute demands pose a major challenge for practical deployment. While studies propose acceleration methods to reduce workload at…

Computer Vision and Pattern Recognition · Computer Science 2025-09-29 Haosong Liu , Yuge Cheng , Wenxuan Miao , Zihan Liu , Aiyue Chen , Jing Lin , Yiwu Yao , Chen Chen , Jingwen Leng , Yu Feng , Minyi Guo

With the emerging diffusion models, recently, text-to-video generation has aroused increasing attention. But an important bottleneck therein is that generative videos often tend to carry some flickers and artifacts. In this work, we propose…

Computer Vision and Pattern Recognition · Computer Science 2024-01-02 Binhui Liu , Xin Liu , Anbo Dai , Zhiyong Zeng , Dan Wang , Zhen Cui , Jian Yang

Diffusion models have established themselves as the de facto primary paradigm in visual generative modeling, revolutionizing the field through remarkable success across various diverse applications ranging from high-quality image synthesis…

Computer Vision and Pattern Recognition · Computer Science 2025-03-11 Zhiyu Tan , WenXu Qian , Hesen Chen , Mengping Yang , Lei Chen , Hao Li

Recent advancements in Virtual Try-On (VTO) have demonstrated exceptional efficacy in generating realistic images and preserving garment details, largely attributed to the robust generative capabilities of text-to-image (T2I) diffusion…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Zhenchen Wan , Yanwu Xu , Zhaoqing Wang , Feng Liu , Tongliang Liu , Mingming Gong

To replicate the success of text-to-image (T2I) generation, recent works employ large-scale video datasets to train a text-to-video (T2V) generator. Despite their promising results, such paradigm is computationally expensive. In this work,…

Computer Vision and Pattern Recognition · Computer Science 2023-03-20 Jay Zhangjie Wu , Yixiao Ge , Xintao Wang , Weixian Lei , Yuchao Gu , Yufei Shi , Wynne Hsu , Ying Shan , Xiaohu Qie , Mike Zheng Shou

Recent text-to-video diffusion models have achieved impressive progress. In practice, users often desire the ability to control object motion and camera movement independently for customized video creation. However, current methods lack the…

Computer Vision and Pattern Recognition · Computer Science 2024-05-07 Shiyuan Yang , Liang Hou , Haibin Huang , Chongyang Ma , Pengfei Wan , Di Zhang , Xiaodong Chen , Jing Liao