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Provenance Brief
Primary Source

Edit3r: Instant 3D Scene Editing from Sparse Unposed Images

In brief:

We present Edit3r, a feed-forward framework that reconstructs and edits 3D scenes in a single pass from unposed, view-inconsistent, instruction-edited images.

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New research could change how AI systems work.

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Major industry investment.

Unlike prior methods requiring per-scene optimization, Edit3r directly predicts instruction-aligned 3D edits, enabling fast and photorealistic rendering without optimization or pose estimation.

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About this source
Source
arXiv cs.CV
Type
Research Preprint
Published
Credibility
Peer-submitted research paper on arXiv

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Edit3r: Instant 3D Scene Editing from Sparse Unposed Images

TL;DR

We present Edit3r, a feed-forward framework that reconstructs and edits 3D scenes in a single pass from unposed, view-inconsistent, instruction-edited images.

Quick Data

Source
https://arxiv.org/abs/2512.25071v1
Type
Research Preprint
Credibility
Peer-submitted research paper on arXiv
Published

Builder Context

Scan abstract → experiments → limitations. Also: verify benchmark methodology; note model size and inference requirements.

Full Analysis

Major industry investment.

Unlike prior methods requiring per-scene optimization, Edit3r directly predicts instruction-aligned 3D edits, enabling fast and photorealistic rendering without optimization or pose estimation.

Open receipts to verify and go deeper.

Source Verification

Source arXiv cs.CV
Type Research Preprint
Tier Primary Source
Assessment Peer-submitted research paper on arXiv
URL https://arxiv.org/abs/2512.25071v1
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