arXiv Computer Vision By Yuqiao Xia, Yuchen Deng, Ziheng Chang, Bingyao Huang

SREdit: Scene Response Guided Appearance Editing for Projection Mapping

Read the original on arXiv Computer Vision →

SREdit is a response‑guided framework that improves projection‑based appearance editing by estimating a scene response map from a reconstructed scene. This map indicates how strongly projected illumination can affect different surface regions, allowing the system to adapt editing strength across the object while preserving registration with the fixed physical surface. Experiments with two pretrained image editors show that SREdit enhances projected appearance quality and reproduction fidelity, adding less than one second per image with negligible VRAM overhead, and participants consistently preferred its results in a user study.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv AI
Aug 26

MatReplace: A Reference-Free, Conditioning-Aligned Benchmark for Material Replacement in Interior Scenes

MatReplace is a new reference‑free benchmark for evaluating material replacement in interior scenes, assessing edits on local material correctness, global lighting harmony, outside preservation, and inside structure. It offers three tracks that vary the conditioning signal—instruction only, instruction plus region mask, and material reference image—allowing systematic comparison of different editing approaches. Results show that while closed‑source editors excel at named‑material rendering, grounding materials from pixel references remains a significant challenge.

By Mingzhe Du, Thong Thanh Nguyen, Nguyen Tran Cong Duy, See-Kiong Ng, Luu Anh Tuan
arXiv Computer Vision
Sep 3

SR-Edit: Region-Aware Image Editing via Self-Refinement

SR-Edit is a new image editing framework that uses iterative self‑refinement to improve fidelity. At each step it extracts precise, self‑consistent region separations from the model’s predictions and then enforces preservation in non‑edit areas with correction updates that stay aligned with the original sampling dynamics. Experiments show that SR‑Edit delivers better preservation and overall image quality than existing editing techniques.

By Andong Wang, Zehua Chen, Yuxuan Jiang, Jun Zhu