We propose a diffusion-based framework for zero-shot image editing that unifies text-guided and reference-guided approaches without requiring fine-tuning. Our method leverages diffusion inversion and timestep-specific null-text embeddings to preserve the structural integrity of the source image. By introducing a stage-wise latent injection strategy-shape injection in early steps and attribute injection in later steps-we enable precise, fine-grained modifications while maintaining global consistency. Cross-attention with reference latents facilitates semantic alignment between the source and reference. Extensive experiments across expression transfer, texture transformation, and style infusion demonstrate state-of-the-art performance, confirming the method's scalability and adaptability to diverse image editing scenarios.
FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing
School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；Lingnan University（岭大大学）；Video Rebirth；The Hong Kong Polytechnic University（香港理工大学）
Tencent AI Lab（腾讯人工智能实验室）；Tsinghua University（清华大学）
Existing methods for face image manipulation generally focus on editing the expression, changing some predefined attributes, or applying different filters. However, users lack the flexibility of controlling the shapes of different semantic facial parts in the generated face. In this paper, we propose an approach to compute a disentangled shape representation for a face image, namely the FaceShapeGene. The proposed FaceShapeGene encodes the shape information of each semantic facial part separately into a 1D latent vector. On the basis of the FaceShapeGene, a novel part-wise face image editing s…
Adaptive and flexible image editing is a desirable function of modern generative models. In this work, we present a generative model with auto-encoder architecture for per-region style manipulation. We apply a code consistency loss to enforce an explicit disentanglement between content and style latent representations, making the content and style of generated samples consistent with their corresponding content and style references. The model is also constrained by a content alignment loss to ensure the foreground editing will not interfere background contents. As a result, given interested re…
ContextDrag: Precise Drag-Based Image Editing via Context-Preserving Token Injection and Position-Aligned Attention
School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子与信息学院）；KlingAI Research, Kuaishou Technology（快手科技KlingAI研究）；The University of Hong Kong（香港大学）；Shenzhen Loop Area Institute（深圳河套学院）
Semantic image synthesis is a process for generating photorealistic images from a single semantic mask. To enrich the diversity of multimodal image synthesis, previous methods have controlled the global appearance of an output image by learning a single latent space. However, a single latent code is often insufficient for capturing various object styles because object appearance depends on multiple factors. To handle individual factors that determine object styles, we propose a class- and layer-wise extension to the variational autoencoder (VAE) framework that allows flexible control over each…
Edit Where You Mean: Region-Aware Adapter Injection for Mask-Free Local Image Editing
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Beijing University of Aeronautics and Astronautics（北京航空航天大学）；Tsinghua University（清华大学）；Peking University（北京大学）；Xiaohongshu Inc.（小红书公司）
Tuning-Free Image Editing with Fidelity and Editability via Unified Latent Diffusion Model
Communication University of China（中国传媒大学）；National University of Singapore（新加坡国立大学）；University of California at Merced（加州大学默塞德分校）；Yonsei University（延世大学）
Object-Aware Background-Controlled Editing via Weighted Velocity Guidance
Carnegie Mellon University（卡内基梅隆大学）；Institute of Computing Technology, Chinese Academy of Sciences（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）
Prompt-Softbox-Prompt: A Free-Text Embedding Control for Image Editing
Shanghai University of Finance and Economics（上海财经大学）；School of Computing and Artificial Intelligence（计算机与人工智能学院）；MoE Key Laboratory of Interdisciplinary Research of Computation and Economics（交叉计算与经济学联合实验室）
MotionEditor: Editing Video Motion via Content-Aware Diffusion
Fudan University（复旦大学）；Shanghai Collaborative Innovation Center of Intelligent Visual Computing（上海智能视觉计算协同创新中心）；Microsoft Research Asia（微软亚洲研究院）；Carnegie Mellon University（卡内基梅隆大学）；Huya Inc.（虎牙公司）
Text-based motion generation models are drawing a surge of interest for their potential for automating the motion-making process in the game, animation, or robot industries. In this paper, we propose a diffusion-based motion synthesis and editing model named FLAME. Inspired by the recent successes in diffusion models, we integrate diffusion-based generative models into the motion domain. FLAME can generate high-fidelity motions well aligned with the given text. Also, it can edit the parts of the motion, both frame-wise and joint-wise, without any fine-tuning. FLAME involves a new transformer-b…
Indian Institute of Technology Guwahati（古瓦哈提印度理工学院）
Detection of different types of image editing operations carried out on an image is an important problem in image forensics. It gives the information about the processing history of an image, and also can expose forgeries present in an image. There have been few methods proposed to detect different types of image editing operations in a single framework. However, all the operations have to be known a priori in the training phase. But, in real-forensics scenarios it may not be possible to know about the editing operations carried out on an image. To solve this problem, we propose a novel deep l…
LIVE: Leveraging Image Manipulation Priors for Instruction-based Video Editing
Nankai University（南开大学）；Pengcheng Laboratory（鹏城实验室）；Kuaishou Technology（快手科技）；Nankai International Advanced Research Institute (SHENZHEN·FUTIAN)（南开国际先进研究院（深圳·福田））
Adobe Research（奥多比研究中心）；University College London（伦敦大学学院）
Image diffusion models, trained on massive image collections, have emerged as the most versatile image generator model in terms of quality and diversity. They support inverting real images and conditional (e.g., text) generation, making them attractive for high-quality image editing applications. We investigate how to use such pre-trained image models for text-guided video editing. The critical challenge is to achieve the target edits while still preserving the content of the source video. Our method works in two simple steps: first, we use a pre-trained structure-guided (e.g., depth) image di…
UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models
The University of British Columbia（不列颠哥伦比亚大学）；Tsinghua University（清华大学）；Snap Inc.（Snap公司）；Vector Institute（向量研究所）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）
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）
EDITBRIDGE: Towards Faithful and Efficient Ultra-High-Resolution Image Editing
School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Qwen Business Unit of Alibaba（阿里巴巴通义千问业务部）；National University of Singapore（新加坡国立大学）
Face attribute editing aims to generate faces with one or multiple desired face attributes manipulated while other details are preserved. Unlike prior works such as GAN inversion, which has an expensive reverse mapping process, we propose a simple feed-forward network to generate high-fidelity manipulated faces. By simply employing some existing and easy-obtainable prior information, our method can control, transfer, and edit diverse attributes of faces in the wild. The proposed method can consequently be applied to various applications such as face swapping, face relighting, and makeup transf…
