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Related papers: VACE: All-in-One Video Creation and Editing

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The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making "what you want is what you see" a reality. Among these, video editing has gained substantial attention and seen a swift rise in…

Computer Vision and Pattern Recognition · Computer Science 2024-07-11 Wenhao Sun , Rong-Cheng Tu , Jingyi Liao , Dacheng Tao

Despite the fact that text-to-video (TTV) model has recently achieved remarkable success, there have been few approaches on TTV for its extension to video editing. Motivated by approaches on TTV models adapting from diffusion-based…

Computer Vision and Pattern Recognition · Computer Science 2023-11-20 Chaehun Shin , Heeseung Kim , Che Hyun Lee , Sang-gil Lee , Sungroh Yoon

Despite the recent strides in video generation, state-of-the-art methods still struggle with elements of visual detail. One particularly challenging case is the class of videos in which the intricate motion of the hand coupled with a mostly…

Computer Vision and Pattern Recognition · Computer Science 2025-07-15 Yayuan Li , Zhi Cao , Jason J. Corso

Recent video editing models have converged on a unified conditioning design: a single diffusion transformer jointly consumes text, source video, and reference images, and one set of weights covers replacement, removal, style transfer, and…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Yongsheng Yu , Ziyun Zeng , Zhiyuan Xiao , Zhenghong Zhou , Hang Hua , Wei Xiong , Jiebo Luo

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

Recent advances in diffusion transformers have shown remarkable generalization in visual synthesis, yet most dense perception methods still rely on text-to-image (T2I) generators designed for stochastic generation. We revisit this paradigm…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Yiqing Shi , Yiren Song , Mike Zheng Shou

Recent advances in video generation have been dominated by diffusion and flow-matching models, which produce high-quality results but remain computationally intensive and difficult to scale. In this work, we introduce VideoAR, the first…

Computer Vision and Pattern Recognition · Computer Science 2026-01-15 Longbin Ji , Xiaoxiong Liu , Junyuan Shang , Shuohuan Wang , Yu Sun , Hua Wu , Haifeng Wang

We present VEnhancer, a generative space-time enhancement framework that improves the existing text-to-video results by adding more details in spatial domain and synthetic detailed motion in temporal domain. Given a generated low-quality…

Computer Vision and Pattern Recognition · Computer Science 2024-07-11 Jingwen He , Tianfan Xue , Dongyang Liu , Xinqi Lin , Peng Gao , Dahua Lin , Yu Qiao , Wanli Ouyang , Ziwei Liu

Recovering 3D scenes from sparse views is a challenging task due to its inherent ill-posed problem. Conventional methods have developed specialized solutions (e.g., geometry regularization or feed-forward deterministic model) to mitigate…

Computer Vision and Pattern Recognition · Computer Science 2025-04-04 Hanyang Wang , Fangfu Liu , Jiawei Chi , Yueqi Duan

Controllable layout generation refers to the process of creating a plausible visual arrangement of elements within a graphic design (e.g., document and web designs) with constraints representing design intentions. Although recent…

Computer Vision and Pattern Recognition · Computer Science 2024-05-17 Jian Chen , Ruiyi Zhang , Yufan Zhou , Rajiv Jain , Zhiqiang Xu , Ryan Rossi , Changyou Chen

We introduce InVi, an approach for inserting or replacing objects within videos (referred to as inpainting) using off-the-shelf, text-to-image latent diffusion models. InVi targets controlled manipulation of objects and blending them…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Nirat Saini , Navaneeth Bodla , Ashish Shrivastava , Avinash Ravichandran , Xiao Zhang , Abhinav Shrivastava , Bharat Singh

Video diffusion generation suffers from critical sampling efficiency bottlenecks, particularly for large-scale models and long contexts. Existing video acceleration methods, adapted from image-based techniques, lack a single-step…

Computer Vision and Pattern Recognition · Computer Science 2025-12-29 Jiaxiang Cheng , Bing Ma , Xuhua Ren , Hongyi Henry Jin , Kai Yu , Peng Zhang , Wenyue Li , Yuan Zhou , Tianxiang Zheng , Qinglin Lu

Diffusion models have made significant strides in image generation, mastering tasks such as unconditional image synthesis, text-image translation, and image-to-image conversions. However, their capability falls short in the realm of video…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Gaurav Shrivastava , Abhinav Shrivastava

Our study introduces a new image-to-video generator called FashionFlow to generate fashion videos. By utilising a diffusion model, we are able to create short videos from still fashion images. Our approach involves developing and connecting…

Computer Vision and Pattern Recognition · Computer Science 2024-01-23 Tasin Islam , Alina Miron , XiaoHui Liu , Yongmin Li

Instruction-based video editing aims to modify an input video according to a natural-language instruction while preserving content fidelity and temporal coherence. However, existing diffusion-based approaches are often trained on paired…

Computer Vision and Pattern Recognition · Computer Science 2025-12-19 Xiaoyan Cong , Haotian Yang , Angtian Wang , Yizhi Wang , Yiding Yang , Canyu Zhang , Chongyang Ma

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

High resolution panoramic video content is paramount for immersive experiences in Virtual Reality, but is non-trivial to collect as it requires specialized equipment and intricate camera setups. In this work, we introduce VideoPanda, a…

Rendering and inverse rendering are pivotal tasks in both computer vision and graphics. The rendering equation is the core of the two tasks, as an ideal conditional distribution transfer function from intrinsic properties to RGB images.…

Computer Vision and Pattern Recognition · Computer Science 2025-03-06 Zhifei Chen , Tianshuo Xu , Wenhang Ge , Leyi Wu , Dongyu Yan , Jing He , Luozhou Wang , Lu Zeng , Shunsi Zhang , Yingcong Chen

Consistent human-centric image and video synthesis aims to generate images or videos with new poses while preserving appearance consistency with a given reference image, which is crucial for low-cost visual content creation. Recent advances…

Computer Vision and Pattern Recognition · Computer Science 2024-12-20 Mingdeng Cao , Chong Mou , Ziyang Yuan , Xintao Wang , Zhaoyang Zhang , Ying Shan , Yinqiang Zheng

Recently, diffusion transformers have gained wide attention with its excellent performance in text-to-image and text-to-vidoe models, emphasizing the need for transformers as backbone for diffusion models. Transformer-based models have…

Computer Vision and Pattern Recognition · Computer Science 2024-04-16 Nithin Gopalakrishnan Nair , Jeya Maria Jose Valanarasu , Vishal M. Patel