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Matching papers

MPIE-Bench: Benchmarking Anatomically Plausible Multi-Person Interaction Editing

37 upvotes · 30 JUL 2026 · Jiajia Lin, Mingxuan Du, Tuowen Zhou et al.

This paper introduces a benchmark to evaluate the performance of models in editing multi-person images, focusing on anatomical and geometric accuracy. Practitioners in the field of computer vision and image editing might care about this research as it aims to improve the quality of human-like images with multiple people.

computer visionimage editingmulti-person scenescontact densitymesh reconstructionanatomical accuracy

FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Generation and Modality Mimicry

31 upvotes · 20 JUL 2026 · Dingyun Zhang, Lixue Gong, Wei Liu

This paper creates a new AI model that can edit and generate videos without needing masks, and can also learn to mimic image editing capabilities. Practitioners might care about this because it could lead to more diverse and realistic video editing data, and enable AI models to understand and generate human-like video editing instructions.

video editinggenerative modelsmultimodal learningimage editingtemporal flow fieldsmodality mimicry

Evaluation-Verification Reward for Consistent Multi-Reference Image Editing

11 upvotes · 31 JUL 2026 · Yingmao Miao, Pengfei Zhang, Xiaochen Lv et al.

This paper develops a new method to evaluate and verify image editing consistency across multiple references, addressing a challenge in reinforcement learning for multi-reference editing. Practitioners may care about this approach as it enables more accurate and reliable reinforcement learning for image editing tasks.

reinforcement learningimage editingmulti-referenceevaluation verification rewardmultimodal large language models