mlsanityData to 7 Oct 2026

TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation

arXiv 2403.12906 ↗First posted 19 Mar 2024cs.CVcited by 14

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Abstract (arXiv)

Texturing 3D humans with semantic UV maps remains a challenge due to the difficulty of acquiring reasonably unfolded UV. Despite recent text-to-3D advancements in supervising multi-view renderings using large text-to-image (T2I) models, issues persist with generation speed, text consistency, and texture quality, resulting in data scarcity among existing datasets. We present TexDreamer, the first zero-shot multimodal high-fidelity 3D human texture generation model. Utilizing an efficient texture adaptation finetuning strategy, we adapt large T2I model to a semantic UV structure while preserving its original generalization capability. Leveraging a novel feature translator module, the trained model is capable of generating high-fidelity 3D human textures from either text or image within seconds. Furthermore, we introduce ArTicuLated humAn textureS (ATLAS), the largest high-resolution (1024 X 1024) 3D human texture dataset which contains 50k high-fidelity textures with text descriptions.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 59 of its 77 references that are in the map. First posted on arXiv 19 Mar 2024.

Builds on

59 of 77 references.

References · 59

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 6; 2026: 3.

14 papers.

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