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arXiv cs.GR
arXiv cs.GR Research
· 3 weeks, 6 days ago • Jiun Tian Hoe, Weipeng Hu, Wei Zhou, Chao Xie, Ziwei Wang, Xudong Jiang, Yap-Peng Tan, Chee Seng Chan

Reference-free Human-Object Interaction Editing

Briefing

InteractEdit is a reference-free human-object interaction editing system built for a problem that sits beyond the usual image-editing buckets. Instead of changing color, style, or swapping an object outright, it tries to transform an existing interaction into a new one while keeping the same subject and object identities intact. That makes it especially relevant to any pipeline that needs controllable scene manipulation rather than one-off generative cleanup.

The core idea is to break a scene into subject, object, and background components so the model can disentangle the interaction from the identities involved. It pairs that with selective inversion and Selective-Rank Adaptation (SeRA), which are used to preserve visual identity from the source image while still leaning on pretrained interaction priors. In practice, that’s an attempt to avoid the common failure mode where an edit works structurally but drifts too far from the original characters or props.

The project also adds IEBench, a benchmark specifically for HOI editing, along with a metric that scores both interaction change and identity preservation together. That matters because these systems can look good in isolation while quietly breaking the thing developers care about most: keeping the right character, prop, or silhouette recognizable after the edit. The paper reports that InteractEdit beats 23 existing methods, positioning it as a strong baseline for future work.

For game developers, the immediate relevance is in tooling and content iteration. Techniques like this could eventually help with concept...

“a strong baseline for future HOI editing research”

— Paper authors · Describing InteractEdit's benchmark results
Original source
Read on arXiv cs.GR
At a glance
what
InteractEdit is a reference-free framework for editing human-object interactions in images while preserving subject and object identity.
who
The work comes from Jiun Tian Hoe, Weipeng Hu, Wei Zhou, Chao Xie, Ziwei Wang, Xudong Jiang, Yap-Peng Tan, and Chee Seng Chan.
when
First submitted on 12 Mar 2025, with a revised version posted on 22 Aug 2026.
impact
It could inform future content-editing and scene-manipulation tools that need controllable changes without identity drift.
Signal Positive

Promising research with practical tooling potential

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