# Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/fully-convolutional-mesh-autoencoder-using-efficient-spatially-varying-kernels/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Yi Zhou,Chenglei Wu,Zimo Li,Chen Cao,Yuting Ye,Jason Saragih,Hao Li,Yaser Sheikh |
| Citations | 47 |
| DOI | 10.48550/arxiv.2006.04325 |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2006.04325 |
| OpenAlex ID | https://openalex.org/W3034157075 |
| Type | preprint |
| Year | 2020 |

## Paper authors

- [Zimo Li](https://scholariq.org/researchers/zimo-li/)

## Paper journal

- [arXiv (Cornell University)](https://scholariq.org/journals/arxiv-cornell-university/)

## Paper primary topic

- [3D Shape Modeling and Analysis](https://scholariq.org/topics/3d-shape-modeling-and-analysis/)

## Paper topics

- [3D Shape Modeling and Analysis](https://scholariq.org/topics/3d-shape-modeling-and-analysis/)
- [Human Pose and Action Recognition](https://scholariq.org/topics/human-pose-and-action-recognition/)
- [Generative Adversarial Networks and Image Synthesis](https://scholariq.org/topics/generative-adversarial-networks-and-image-synthesis/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.