2025/09/28 by Boseong Felipe Jeon, Jeon, Boseong, Junghyuk Lee +10
Computer Science · #Advanced Malware Detection Techniques #Artificial Intelligence (cs.AI) #Cloud Data Security Solutions #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #User Authentication and Security Systems
paper · pdf · doi:10.48550/arxiv.2509.23708
openalex publication_date 2025/09/28 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28
Recent works on object removal and insertion have enhanced their performance by handling object effects such as shadows and reflections, using diffusion models trained on counterfactual datasets. However, the performance impact of applying classifier-free guidance to handle object effects across removal and insertion tasks within a unified model remains largely unexplored. To address this gap and improve efficiency in composite editing, we propose CrimEdit, which jointly trains the task embeddings for removal and insertion within a single model and leverages them in a classifier-free guidance scheme -- enhancing the removal of both objects and their effects, and enabling controllable synthesis of object effects during insertion. CrimEdit also extends these two task prompts to be applied to spatially distinct regions, enabling object movement (repositioning) within a single denoising step. By employing both guidance techniques, extensive experiments show that CrimEdit achieves superior object removal, controllable effect insertion, and efficient object movement without requiring additional training or separate removal and insertion stages.