SMPL-GPTexture: Dual-View 3D Human Texture Estimation using Text-to-Image Generation Models

[2504.13378] SMPL-GPTexture: Dual-View 3D Human Texture Estimation using Text-to-Image Generation Models Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Graphics arXiv:2504.13378 (cs) [Submitted on 17 Apr 2025 ( v1 ), last revised 9 Apr 2026 (this version, v2)] Title: SMPL-GPTexture: Dual-View 3D Human Texture Estimation using Text-to-Image Generation Models Authors: Mingxiao Tu , Shuchang Ye , Hoijoon Jung , Jinman Kim View a PDF of the paper titled SMPL-GPTexture: Dual-View 3D Human Texture Estimation using Text-to-Image Generation Models, by Mingxiao Tu and 3 other authors View PDF HTML (experimental) Abstract: Generating high-quality, photorealistic textures for 3D human avatars remains a fundamental yet challenging task in computer vision and multimedia field. However, real paired front and back images of human subjects are rarely available with privacy, ethical and cost of acquisition, which restricts scalability of the data. Additionally, learning priors from image inputs using deep generative models, such as GANs or diffusion models, to infer unseen regions such as the human back often leads to artifacts, structural inconsistencies, or loss of fine-grained detail. To address these issues, we present SMPL-GPTexture (skinned multi-person linear model - general purpose Texture), a novel pipeline that takes natural language prompts as input and leverages a state-of-the-art text-to-image generation model to produce paired high-resolution front and back images of a human subject as the starting point for texture estimation. Using the generated paired dual-view images, we first employ a human mesh recovery model to obtain a robust 2D-to-3D SMPL alignment between image pixels and the 3D model's UV coordinates for each views. Second, we use an inverted rasterization technique that explicitly projects the observed colour from the input images into the UV space, thereby producing accurate, complete texture maps. Finally, we apply a diffusion-based inpainting module to fill in the missing regions, and the fusion mechanism then combines these results into a unified full texture map. Extensive experiments shows that our SMPL-GPTexture can generate high resolution texture aligned with user's prompts. Subjects: Graphics (cs.GR) ; Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2504.13378 [cs.GR] (or arXiv:2504.13378v2 [cs.GR] for this version) https://doi.org/10.48550/arXiv.2504.13378 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Shuchang Ye [ view email ] [v1] Thu, 17 Apr 2025 23:28:38 UTC (6,559 KB) [v2] Thu, 9 Apr 2026 03:17:26 UTC (39,653 KB) Full-text links: Access Paper: View a PDF of the paper titled SMPL-GPTexture: Dual-View 3D Human Texture Estimation using Text-to-Image Generation Models, by Mingxiao Tu and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.GR < prev | next > new | recent | 2025-04 Change to browse by: cs cs.CV References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• BodyMAE: A Surface-Area Aware Masked Autoencoder for Body Composition Estimation from 3D Body Scans#341962
• Sound-based Multi-Person 3D Pose Estimation#660837
• FMReward: Aligning and Evaluating Audio-Driven 3D Facial Animation with Human Preferences#615270
• FMReward: Aligning and Evaluating Audio-Driven 3D Facial Animation with Human Preferences#614303
• ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization#1013394