# Coordinate-based Texture Inpainting for Pose-Guided Image Generation

arXiv: https://arxiv.org/abs/1811.11459 · HTML: https://mlsanity.com/paper/1811.11459/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

## Built on by (most cited first, 10 of 10)

- [StylePeople: A Generative Model of Fullbody Human Avatars](https://mlsanity.com/paper/2104.08363/) (2021)
- [ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priors](https://mlsanity.com/paper/2109.07001/) (2021)
- [3D-Aware Semantic-Guided Generative Model for Human Synthesis](https://mlsanity.com/paper/2112.01422/) (2021)
- [Sparse to Dense Motion Transfer for Face Image Animation](https://mlsanity.com/paper/2109.00471/) (2021)
- [SPG-VTON: Semantic Prediction Guidance for Multi-pose Virtual Try-on](https://mlsanity.com/paper/2108.01578/) (2021)
- [TemporalUV: Capturing Loose Clothing with Temporally Coherent UV Coordinates](https://mlsanity.com/paper/2204.03671/) (2022)
- [Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization](https://mlsanity.com/paper/2105.14739/) (2021)
- [HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion](https://mlsanity.com/paper/2410.21789/) (2024)
- [Self-Supervised Equivariant Scene Synthesis from Video](https://mlsanity.com/paper/2102.00863/) (2021)
- [Advances in Neural 3D Mesh Texturing: A Survey](https://mlsanity.com/paper/2606.00137/) (2026)

## Abstract (arXiv)

We present a new deep learning approach to pose-guided resynthesis of human photographs. At the heart of the new approach is the estimation of the complete body surface texture based on a single photograph. Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body. Rather than working directly with colors of texture elements, the inpainting network estimates an appropriate source location in the input image for each element of the body surface. This correspondence field between the input image and the texture is then further warped into the target image coordinate frame based on the desired pose, effectively establishing the correspondence between the source and the target view even when the pose change is drastic. The final convolutional network then uses the established correspondence and all other available information to synthesize the output image. A fully-convolutional architecture with deformable skip connections guided by the estimated correspondence field is used. We show state-of-the-art result for pose-guided image synthesis. Additionally, we demonstrate the performance of our system for garment transfer and pose-guided face resynthesis.