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arXiv Computer Vision By Xiaolin Li, Zihao Huang, Fanpeng Meng, Chen Yang, Guanjun Wu, Kai Zhang, Jiemin Fang, Qi Tian

Gate3DGen: A Pre-processing Model that Selectively Purifies Real-World Images for 3D Generation

Read the original on arXiv Computer Vision →

Gate3DGen is a selective pre‑processing model that improves single‑image 3D generation by purifying real‑world images with uneven illumination, blur, or occlusions. It uses a degradation‑aware router to bypass clean inputs and a 3D‑oriented one‑step purifier that preserves object identity, contours, and textures. The method is trained with a synthetic data engine and demonstrates significant improvements on synthetic benchmarks and real photographs, reducing mesh distance by 61.5% and receiving positive human and VLM evaluations.

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.

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