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This paper proposes ProEdit - a simple yet effective framework for high-quality 3D scene editing guided by diffusion distillation in a novel progressive manner. Inspired by the crucial observation that multi-view inconsistency in scene…

Computer Vision and Pattern Recognition · Computer Science 2024-11-08 Jun-Kun Chen , Yu-Xiong Wang

Diffusion-based point editing methods have gained significant traction in image editing tasks due to their ability to manipulate image semantics and fine details by applying localized perturbations on the manifold of noise latent. However,…

Computer Vision and Pattern Recognition · Computer Science 2026-05-14 Haoyang Hu , Masataka Seo , Yen-Wei Chen

Modern Text-to-Image (T2I) Diffusion models have revolutionized image editing by enabling the generation of high-quality photorealistic images. While the de facto method for performing edits with T2I models is through text instructions,…

Computer Vision and Pattern Recognition · Computer Science 2024-11-07 Ashutosh Srivastava , Tarun Ram Menta , Abhinav Java , Avadhoot Jadhav , Silky Singh , Surgan Jandial , Balaji Krishnamurthy

Toon shading is a type of non-photorealistic rendering task of animation. Its primary purpose is to render objects with a flat and stylized appearance. As diffusion models have ascended to the forefront of image synthesis methodologies,…

Computer Vision and Pattern Recognition · Computer Science 2024-01-30 Zhongjie Duan , Chengyu Wang , Cen Chen , Weining Qian , Jun Huang

Recent advances in text-to-video (T2V) diffusion models have significantly enhanced the quality of generated videos. However, their capability to produce explicit or harmful content introduces new challenges related to misuse and potential…

Computer Vision and Pattern Recognition · Computer Science 2025-09-30 Xiaoyu Ye , Songjie Cheng , Yongtao Wang , Yajiao Xiong , Yishen Li

Recent advances in diffusion models have revolutionized text-guided image editing, yet existing editing methods face critical challenges in hyperparameter identification. To get the reasonable editing performance, these methods often…

Computer Vision and Pattern Recognition · Computer Science 2025-10-08 Chau Pham , Quan Dao , Mahesh Bhosale , Yunjie Tian , Dimitris Metaxas , David Doermann

Recent advances in training-free attention control methods have enabled flexible and efficient text-guided editing capabilities for existing generation models. However, current approaches struggle to simultaneously deliver strong editing…

Computer Vision and Pattern Recognition · Computer Science 2025-10-21 Zixin Yin , Ling-Hao Chen , Lionel Ni , Xili Dai

Video editing has recently achieved remarkable progress with diffusion-based generative models, enabling diverse object-level manipulations from natural language instructions. However, existing methods often struggle under occlusion,…

Computer Vision and Pattern Recognition · Computer Science 2026-05-28 Lin Liu , Zhihan Xiao , Haohang Xu , Rong Cong , Zhibo Zhang , Xiaopeng Zhang , Qi Tian

We introduce Emu Video Edit (EVE), a model that establishes a new state-of-the art in video editing without relying on any supervised video editing data. To develop EVE we separately train an image editing adapter and a video generation…

Computer Vision and Pattern Recognition · Computer Science 2024-03-26 Uriel Singer , Amit Zohar , Yuval Kirstain , Shelly Sheynin , Adam Polyak , Devi Parikh , Yaniv Taigman

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

Diffusion-based Image Editing has achieved significant success in recent years. However, it remains challenging to achieve high-quality image editing while maintaining the background similarity without sacrificing speed or memory…

Graphics · Computer Science 2025-09-03 Siyi Liu , Weiming Chen , Yushun Tang , Zhihai He

Current diffusion-based video editing primarily focuses on local editing (\textit{e.g.,} object/background editing) or global style editing by utilizing various dense correspondences. However, these methods often fail to accurately edit the…

Computer Vision and Pattern Recognition · Computer Science 2024-03-26 Xiangpeng Yang , Linchao Zhu , Hehe Fan , Yi Yang

Diffusion Transformers (DiTs) have demonstrated remarkable scalability and quality in image and video generation, prompting growing interest in extending them to controllable generation and editing tasks. However, compared to the image…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Ruonan Yu , Zhenxiong Tan , Zigeng Chen , Songhua Liu , Xinchao Wang

Text-guided non-rigid editing involves complex edits for input images, such as changing motion or compositions within their surroundings. Since it requires manipulating the input structure, existing methods often struggle with preserving…

Computer Vision and Pattern Recognition · Computer Science 2024-10-17 Yunji Jung , Seokju Lee , Tair Djanibekov , Hyunjung Shim , Jong Chul Ye

Recent advances in image editing, driven by image diffusion models, have shown remarkable progress. However, significant challenges remain, as these models often struggle to follow complex edit instructions accurately and frequently…

Computer Vision and Pattern Recognition · Computer Science 2025-03-21 Noam Rotstein , Gal Yona , Daniel Silver , Roy Velich , David Bensaïd , Ron Kimmel

Instruction-based image editing aims to modify source content according to textual instructions. However, existing methods built upon flow matching often struggle to maintain consistency in non-edited regions due to denoising-induced…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Zongqing Li , Zhihui Liu , Yujie Xie , Shansiyuan Wu , Hongshen Lv , Songzhi Su

Diffusion models have significantly improved the performance of image editing. Existing methods realize various approaches to achieve high-quality image editing, including but not limited to text control, dragging operation, and…

Computer Vision and Pattern Recognition · Computer Science 2024-06-05 Ling Yang , Bohan Zeng , Jiaming Liu , Hong Li , Minghao Xu , Wentao Zhang , Shuicheng Yan

Despite the remarkable progress in text-driven video editing, generating coherent non-rigid deformations remains a critical challenge, often plagued by physical distortion and temporal flicker. To bridge this gap, we propose NRVBench, the…

Computer Vision and Pattern Recognition · Computer Science 2026-01-27 Bingzheng Qu , Kehai Chen , Xuefeng Bai , Jun Yu , Min Zhang

Despite recent advances in UNet-based image editing, methods for shape-aware object editing in high-resolution images are still lacking. Compared to UNet, Diffusion Transformers (DiT) demonstrate superior capabilities to effectively capture…

Computer Vision and Pattern Recognition · Computer Science 2024-11-08 Kunyu Feng , Yue Ma , Bingyuan Wang , Chenyang Qi , Haozhe Chen , Qifeng Chen , Zeyu Wang

We propose a method for adding sound-guided visual effects to specific regions of videos with a zero-shot setting. Animating the appearance of the visual effect is challenging because each frame of the edited video should have visual…

Computer Vision and Pattern Recognition · Computer Science 2023-04-17 Seung Hyun Lee , Sieun Kim , Innfarn Yoo , Feng Yang , Donghyeon Cho , Youngseo Kim , Huiwen Chang , Jinkyu Kim , Sangpil Kim