# Multi-View Image Generation from a Single-View

arXiv: https://arxiv.org/abs/1704.04886 · HTML: https://mlsanity.com/paper/1704.04886/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2017.

## Built on by (most cited first, 27 of 27)

- [Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era](https://mlsanity.com/paper/2305.06131/) (2023)
- [Neural Re-Rendering of Humans from a Single Image](https://mlsanity.com/paper/2101.04104/) (2021)
- [Learning Semantic Person Image Generation by Region-Adaptive Normalization](https://mlsanity.com/paper/2104.06650/) (2021)
- [Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion](https://mlsanity.com/paper/2209.13360/) (2022)
- [HDFormer: High-order Directed Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2302.01825/) (2023)
- [TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models](https://mlsanity.com/paper/2411.18350/) (2024)
- [Layout-Guided Novel View Synthesis from a Single Indoor Panorama](https://mlsanity.com/paper/2103.17022/) (2021)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models](https://mlsanity.com/paper/2310.18332/) (2023)
- [PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation](https://mlsanity.com/paper/2308.09678/) (2023)
- [TIPS: Text-Induced Pose Synthesis](https://mlsanity.com/paper/2207.11718/) (2022)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [Multi-scale Attention Guided Pose Transfer](https://mlsanity.com/paper/2202.06777/) (2022)
- [A Shape-Aware Retargeting Approach to Transfer Human Motion and Appearance in Monocular Videos](https://mlsanity.com/paper/2103.15596/) (2021)
- [Progressive and Aligned Pose Attention Transfer for Person Image Generation](https://mlsanity.com/paper/2103.11622/) (2021)
- [Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis](https://mlsanity.com/paper/2303.01178/) (2023)
- [Novel View Synthesis from a Single RGBD Image for Indoor Scenes](https://mlsanity.com/paper/2311.01065/) (2023)
- [Scene Aware Person Image Generation through Global Contextual Conditioning](https://mlsanity.com/paper/2206.02717/) (2022)
- [Render In-between: Motion Guided Video Synthesis for Action Interpolation](https://mlsanity.com/paper/2111.01029/) (2021)
- [Self-supervised Multi-view Clustering in Computer Vision: A Survey](https://mlsanity.com/paper/2309.09473/) (2023)
- [AdaptiveFusion: Adaptive Multi-Modal Multi-View Fusion for 3D Human Body Reconstruction](https://mlsanity.com/paper/2409.04851/) (2024)
- [Multi-View Image-to-Image Translation Supervised by 3D Pose](https://mlsanity.com/paper/2104.05779/) (2021)
- [Adaptive Appearance Rendering](https://mlsanity.com/paper/2104.11931/) (2021)
- [Sparse Pose Trajectory Completion](https://mlsanity.com/paper/2105.00125/) (2021)
- [Survey on Controlable Image Synthesis with Deep Learning](https://mlsanity.com/paper/2307.10275/) (2023)
- [Simple Yet Effective Selective Imputation for Incomplete Multi-view Clustering](https://mlsanity.com/paper/2512.10327/) (2025)
- [RAVA: Retrieval-Augmented Viewpoint Alignment for Subject-Driven Image Generation](https://mlsanity.com/paper/2606.17619/) (2026)

## Abstract (arXiv)

This paper addresses a challenging problem -- how to generate multi-view cloth images from only a single view input. To generate realistic-looking images with different views from the input, we propose a new image generation model termed VariGANs that combines the strengths of the variational inference and the Generative Adversarial Networks (GANs). Our proposed VariGANs model generates the target image in a coarse-to-fine manner instead of a single pass which suffers from severe artifacts. It first performs variational inference to model global appearance of the object (e.g., shape and color) and produce a coarse image with a different view. Conditioned on the generated low resolution images, it then proceeds to perform adversarial learning to fill details and generate images of consistent details with the input. Extensive experiments conducted on two clothing datasets, MVC and DeepFashion, have demonstrated that images of a novel view generated by our model are more plausible than those generated by existing approaches, in terms of more consistent global appearance as well as richer and sharper details.