Multi-Object Reward-Guided Optimization for Empty-Room-to-Furnished-Scene Image Editing

Recent instruction-guided image editing models support flexible single-object manipulation, but multi-object editing in one scene remains challenging. In empty-room-to-furnished-scene editing, we aim to insert all requested furniture items, preserve source identity, control spatial placement, and maintain plausible room layout. Existing multi-reference editors often treat furniture references as a holistic cue, causing missing objects, identity drift, counting errors, unrealistic placement, and background damage. In this paper, we propose a novel method of Multi-Object Reward-Guided Optimization (MORGO), which formulates the task as multi-object furniture fusion. It builds a reference-guided synthetic dataset with empty rooms, moodboards, instructions, pseudo-furnished targets, and object-level metadata for source tracing. Furthermore, we develop a source-aware VQA evaluator for furniture-level rewards, supported by classical image-similarity metrics as an auxiliary audit. This evaluation is further converted into a reward signal for Direct Preference Optimization (DPO) fine-tuning. We have built a dataset, which contains 5,000 images of furniture, varying randomly from 1 to 9 objects in an image. Experimental results show that our method achieves the evaluation metrics R total = 0.749, that outperforms the existing methods of Qwen base and OmniGen2.