Latent Diffusion Models (LDMs) have markedly advanced the quality of image inpainting and local editing. However, the inherent latent compression often introduces pixel-level inconsistencies, such as chromatic shifts, texture mismatches, and visible seams along editing boundaries. Existing remedies, including background-conditioned latent decoding and pixel-space harmonization, usually fail to fully eliminate these artifacts in practice and do not generalize well across different latent representations or tasks. We introduce PixPerfect, a pixel-level refinement framework that delivers seamless, high-fidelity local edits across diverse LDM architectures and tasks. PixPerfect leverages (i) a differentiable discriminative pixel space that amplifies and suppresses subtle color and texture discrepancies, (ii) a comprehensive artifact simulation pipeline that exposes the refiner to realistic local editing artifacts during training, and (iii) a direct pixel-space refinement scheme that ensures broad applicability across diverse latent representations and tasks. Extensive experiments on inpainting, object removal, and insertion benchmarks demonstrate that PixPerfect substantially enhances perceptual fidelity and downstream editing performance, establishing a new standard for robust and high-fidelity localized image editing.
ASTRA: Let Arbitrary Subjects Transform in Video Editing
NExT++ Research Centre, National University of Singapore（新加坡国立大学NExT++研究中心）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；College of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University（南京林业大学信息科学与技术学院及人工智能学院）
CannyEdit: Selective Canny Control and Dual-Prompt Guidance for Training-Free Image Editing
Huawei Hong Kong AI Framework & Data Technologies Lab（华为香港AI框架与数据技术实验室）；The Hong Kong University of Science and Technology（香港科技大学）；Shanghai University of Finance and Economics（上海财经大学）
Task-Oriented Diffusion Inversion for High-Fidelity Text-based Editing
The University of Hong Kong（香港大学）；Shanghai AI Lab（上海人工智能实验室）；Ocean University of China（中国海洋大学）；Singapore Management University（新加坡管理大学）；South China University of Technology（华南理工大学）
JAVEDIT: Joint Audio-Visual Instruction-Guided Video Editing with Agentic Data Curation
Zhejiang University（浙江大学）；Tencent Youtu Lab（腾讯优图实验室）；Nanjing University（南京大学）；University of Auckland（奥克兰大学）；Fudan University（复旦大学）；National University of Singapore（新加坡国立大学）
StreamEdit: Training-Free Video Editing via Few-Step Streaming Video Generation
The University of British Columbia（不列颠哥伦比亚大学）；ETH Zürich（苏黎世联邦理工学院）；McMaster University（麦马斯特大学）；Vector Institute（向量研究所）；Canada CIFAR AI Chair（加拿大 CIFAR 人工智能主席）
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）
PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data
National University of Singapore（新加坡国立大学）；Shanghai Jiao Tong University（上海交通大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Tiamat（提亚马特）
