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

Xiaolin Li, Zihao Huang, Fanpeng Meng, Chen Yang, Guanjun Wu, Kai Zhang, Jiemin Fang, Qi Tian · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31649 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

A geração de mes-imagem 3D tem alcançado alta fidelidade em benchmarks curados, mas degrada acentuadamente em imagens do mundo real com iluminação desigual, obscuridade e ocluções. Purificar essas entradas requer decidir quando intervir e preservar as indicações visuais necessárias para a geração 3D, enquanto deixa as entradas limpas inalteradas. Introduzimos o Gate3DGen, um modelo de pré-processamento seletivo que leva as entradas de imagem para os geradores 3D existentes sem retrabalhá-las. Um roteador consciente da degradação contorna as entradas classificadas como limpas e extrai recursos para orientar a purificação das entradas restantes. Condicionado a essas características, um purificador de um único passo 3D purifica as entradas degradadas

Machine translation (Meta M2M100); the English original follows.

Abstract

Single-image 3D generation has achieved high fidelity on curated benchmarks, yet degrades sharply on real-world images with uneven illumination, blur, and occlusions. Purifying such inputs requires deciding when to intervene and preserving the visual cues needed for 3D generation, while leaving clean inputs unchanged. We introduce Gate3DGen, a selective pre-processing model that gates image inputs to existing 3D generators without retraining them. A degradation-aware router bypasses inputs classified as clean and extracts features to guide purification of the remaining inputs. Conditioned on these features, a 3D-oriented one-step purifier purifies degraded inputs with spatial losses that encourage preservation of object identity, contours, and textural cues. To provide paired supervision, we construct a synthetic data engine that renders clean views of 3D assets and synthesises composite degradations. On the synthetic benchmark, the purifier improves image fidelity and downstream 3D geometry in only 2.3s on average, reducing TRELLIS.2 mesh distance by 61.5% relative to degraded inputs. VLM evaluation and human preferences on real photographs further support identity preservation and degradation correction. More demos and code are available at https://supra-3d.github.io/gate3dgen.

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Topics: Object (grammar), Computer science, Artificial intelligence, Engineering, Computer vision, Fidelity

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