Instruction Guided Multi Object Image Editing with Quantity and Layout Consistency
Instruction driven image editing with standard CLIP text encoders often fails in complex scenes with many objects. We present QL-Adapter, a framework for multiple object editing that tackles two challenges: enforcing object counts and spatial layouts, and accommodating diverse categories. QL-Adapter consists of two core modules: the Image-Layout Fusion Module (ILFM) and the Cross-Modal Augmentation Module (CMAM). ILFM fuses layout priors with ViT patch tokens from the CLIP image encoder to strengthen spatial structure understanding. CMAM injects image features into the text branch to enrich textual embeddings and improve instruction following. We further build QL-Dataset, a benchmark that spans broad category, layout, and count variations, and define the task of quantity and layout consistent image editing (QL-Edit). Extensive experiments show that QL-Adapter achieves state of the art performance on QL-Edit and significantly outperforms existing models.
Code (0)
등록된 구현이 없습니다.
Tasks
Instruction FollowingImage EditingSimilar Papers 제목 키워드 기반
CompBench: Benchmarking Complex Instruction-guided Image Editing
While real-world applications increasingly demand intricate scene manipulation, existing instruction-guided image editing benchmarks often oversimplify task complexity and lack comprehensive, fine-grained instructions. T…
BenchmarkingInstruction FollowingImage Inpainting Models are Effective Tools for Instruction-guided Image Editing
This is the technique report for the winning solution of the CVPR2024 GenAI Media Generation Challenge Workshop's Instruction-guided Image Editing track. Instruction-guided image editing has been largely studied in recen…
Image GenerationImage InpaintingOBJECT 3DIT: Language-guided 3D-aware Image Editing
Existing image editing tools, while powerful, typically disregard the underlying 3D geometry from which the image is projected. As a result, edits made using these tools may become detached from the geometry and lighting…
3D geometryObjectGIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing
Editing images using natural language instructions has become a natural and expressive way to modify visual content; yet, evaluating the performance of such models remains challenging. Existing evaluation approaches ofte…
Instruction FollowingMultiple-choicetext-guided-image-editingtext similarityInstruction-based Image Editing with Planning, Reasoning, and Generation
Editing images via instruction provides a natural way to generate interactive content, but it is a big challenge due to the higher requirement of scene understanding and generation. Prior work utilizes a chain of large l…
Object SegmentationScene UnderstandingImage Editing