# Learned Multi-View Texture Super-Resolution

arXiv: https://arxiv.org/abs/2001.04775 · HTML: https://mlsanity.com/paper/2001.04775/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2020.

## Built on by (most cited first, 7 of 7)

- [Attention-based Multi-Reference Learning for Image Super-Resolution](https://mlsanity.com/paper/2108.13697/) (2021)
- [Super-resolution 3D Human Shape from a Single Low-Resolution Image](https://mlsanity.com/paper/2208.10738/) (2022)
- [Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution](https://mlsanity.com/paper/2207.08601/) (2022)
- [Super-Resolution Appearance Transfer for 4D Human Performances](https://mlsanity.com/paper/2108.13739/) (2021)
- [Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images](https://mlsanity.com/paper/2505.24799/) (2025)
- [Advances in Neural 3D Mesh Texturing: A Survey](https://mlsanity.com/paper/2606.00137/) (2026)
- [Texture++: Elevating 3D Asset Texture Resolution with a Region-Aware Diffusion Model](https://mlsanity.com/paper/2607.21504/) (2026)

## Abstract (arXiv)

We present a super-resolution method capable of creating a high-resolution texture map for a virtual 3D object from a set of lower-resolution images of that object. Our architecture unifies the concepts of (i) multi-view super-resolution based on the redundancy of overlapping views and (ii) single-view super-resolution based on a learned prior of high-resolution (HR) image structure. The principle of multi-view super-resolution is to invert the image formation process and recover the latent HR texture from multiple lower-resolution projections. We map that inverse problem into a block of suitably designed neural network layers, and combine it with a standard encoder-decoder network for learned single-image super-resolution. Wiring the image formation model into the network avoids having to learn perspective mapping from textures to images, and elegantly handles a varying number of input views. Experiments demonstrate that the combination of multi-view observations and learned prior yields improved texture maps.