Instruction-guided image editing has seen remarkable progress with models like FLUX.2 and Qwen-Image-Edit, yet they still struggle with complex scenarios with multiple similar instances each requiring individual edits. We observe that state-of-the-art models suffer from severe over-editing and spatial misalignment when faced with multiple identical instances and composite instructions. To this end, we introduce a comprehensive benchmark specifically designed to evaluate fine-grained consistency in multi-instance and multi-instruction settings. To address the failures of existing methods observed in our benchmark, we propose Multi-Instance Regional Alignment via Guided Editing (MIRAGE), a training-free framework that enables precise, localized editing. By leveraging a vision-language model to parse complex instructions into regional subsets, MIRAGE employs a multi-branch parallel denoising strategy. This approach injects latent representations of target regions into the global representation space while maintaining background integrity through a reference trajectory. Extensive evaluations on MIRA-Bench and RefEdit-Bench demonstrate that our framework significantly outperforms existing methods in achieving precise instance-level modifications while preserving background consistency. Our benchmark and code are available at https://github.com/ZiqianLiu666/MIRAGE.
Preference Alignment on Diffusion Model: A Comprehensive Survey for Image Generation and Editing
University of Liverpool（利物浦大学）；Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；University of Edinburgh（爱丁堡大学）；University of Bristol（布里斯托大学）；University of Exeter（埃克塞特大学）
DirectEdit: Step-Level Accurate Inversion for Flow-Based Image Editing
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China（多媒体软件国家工程研究中心，计算机科学学院，武汉大学，中国武汉）
University of Science and Technology of China（中国科学技术大学）；Nanyang Technological University（南洋理工大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Aarhus University（奥胡斯大学）
Anchor Token Matching: Implicit Structure Locking for Training-free AR Image Editing
VCIP, College of Computer Science, Nankai University（VCIP，计算机科学学院，南开大学）；NKIARI, Shenzhen Futian（NKIARI，深圳福田）；Computer Vision Center, Universitat Autònoma de Barcelona（计算机视觉中心，巴塞罗那自治大学）；City University of Hong Kong (Dongguan)（香港城市大学（东莞））
Towards Small Object Editing: A Benchmark Dataset and A Training-Free Approach
Zhejiang University of Technology（浙江工业大学）；University of Sydney（悉尼大学）；The University of Hong Kong（香港大学）；Jilin University（吉林大学）；National University of Singapore（新加坡国立大学）
We introduce MotionCLIP, a 3D human motion auto-encoder featuring a latent embedding that is disentangled, well behaved, and supports highly semantic textual descriptions. MotionCLIP gains its unique power by aligning its latent space with that of the Contrastive Language-Image Pre-training (CLIP) model. Aligning the human motion manifold to CLIP space implicitly infuses the extremely rich semantic knowledge of CLIP into the manifold. In particular, it helps continuity by placing semantically similar motions close to one another, and disentanglement, which is inherited from the CLIP-space stru…
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model
The University of Sydney（悉尼大学）；University of Malaya（马来亚大学）；Suzhou Yierqi（苏州意尔奇）；The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；University of Wollongong（伍伦贡大学）
Sketch3DVE: Sketch-based 3D-Aware Scene Video Editing
Institute of Computing Technology, Chinese Academy of Sciences, China（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Hong Kong University of Science（香港科学大学）
FaceEditTalker: Controllable Talking Head Generation with Facial Attribute Editing
Xi’an Key Laboratory of Big Data and Intelligent Vision, Xidian University, Xi’an 710071, China（西安大数据与智能视觉重点实验室，西安电子科技大学，西安710071，中国）；Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an 710071, China（协同智能系统重点实验室，教育部，西安电子科技大学，西安710071，中国）；School of Computer Science and Technology, Xidian University, Xi’an 710071, China（计算机科学与技术学院，西安电子科技大学，西安710071，中国）
TaleForge: Interactive Multimodal System for Personalized Story Creation
1 University of Science, VNU-HCM, Ho Chi Minh City, Vietnam；2 Vietnam National University, Ho Chi Minh City, Vietnam；3 Department of Computer Science \ of Dayton Ohio, United States
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Computer Science & Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）
Most existing style transfer methods follow the assumption that styles can be represented with global statistics (e.g., Gram matrices or covariance matrices), and thus address the problem by forcing the output and style images to have similar global statistics. An alternative is the assumption of local style patterns, where algorithms are designed to swap similar local features of content and style images. However, the limitation of these existing methods is that they neglect the semantic structure of the content image which may lead to corrupted content structure in the output. In this paper,…
