# Re-Identification Supervised Texture Generation

arXiv: https://arxiv.org/abs/1904.03385 · HTML: https://mlsanity.com/paper/1904.03385/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2019.

## Built on by (most cited first, 19 of 19)

- [3D Human Texture Estimation from a Single Image with Transformers](https://mlsanity.com/paper/2109.02563/) (2021)
- [SMPLitex: A Generative Model and Dataset for 3D Human Texture Estimation from Single Image](https://mlsanity.com/paper/2309.01855/) (2023)
- [3D Human Pose, Shape and Texture from Low-Resolution Images and Videos](https://mlsanity.com/paper/2103.06498/) (2021)
- [Tracking People with 3D Representations](https://mlsanity.com/paper/2111.07868/) (2021)
- [Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification](https://mlsanity.com/paper/2204.02611/) (2022)
- [TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation](https://mlsanity.com/paper/2403.12906/) (2024)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation](https://mlsanity.com/paper/2404.15276/) (2024)
- [TGHop: An Explainable, Efficient and Lightweight Method for Texture Generation](https://mlsanity.com/paper/2107.04020/) (2021)
- [Generating Texture for 3D Human Avatar from a Single Image using Sampling and Refinement Networks](https://mlsanity.com/paper/2305.00936/) (2023)
- [UVMap-ID: A Controllable and Personalized UV Map Generative Model](https://mlsanity.com/paper/2404.14568/) (2024)
- [Open-World Pose Transfer via Sequential Test-Time Adaption](https://mlsanity.com/paper/2303.10945/) (2023)
- [Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucinations](https://mlsanity.com/paper/2203.06901/) (2022)
- [TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network](https://mlsanity.com/paper/2208.08768/) (2022)
- [Refining 3D Human Texture Estimation from a Single Image](https://mlsanity.com/paper/2303.03471/) (2023)
- [SMPL-GPTexture: Dual-View 3D Human Texture Estimation using Text-to-Image Generation Models](https://mlsanity.com/paper/2504.13378/) (2025)
- [Neural Texture Puppeteer: A Framework for Neural Geometry and Texture Rendering of Articulated Shapes, Enabling Re-Identification at Interactive Speed](https://mlsanity.com/paper/2311.17109/) (2023)
- [TextureMeDefect: LLM-based Defect Texture Generation for Railway Components on Mobile Devices](https://mlsanity.com/paper/2410.18085/) (2024)
- [Advances in Neural 3D Mesh Texturing: A Survey](https://mlsanity.com/paper/2606.00137/) (2026)

## Abstract (arXiv)

The estimation of 3D human body pose and shape from a single image has been extensively studied in recent years. However, the texture generation problem has not been fully discussed. In this paper, we propose an end-to-end learning strategy to generate textures of human bodies under the supervision of person re-identification. We render the synthetic images with textures extracted from the inputs and maximize the similarity between the rendered and input images by using the re-identification network as the perceptual metrics. Experiment results on pedestrian images show that our model can generate the texture from a single image and demonstrate that our textures are of higher quality than those generated by other available methods. Furthermore, we extend the application scope to other categories and explore the possible utilization of our generated textures.