# Attribute2Image: Conditional Image Generation from Visual Attributes

arXiv: https://arxiv.org/abs/1512.00570 · HTML: https://mlsanity.com/paper/1512.00570/

## Key facts

- Cited by 93 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2015.

## Built on by (most cited first, 30 of 93)

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- [Conditional LoRA Parameter Generation](https://mlsanity.com/paper/2408.01415/) (2024)
- [ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories](https://mlsanity.com/paper/2302.02373/) (2023)
- [Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Models](https://mlsanity.com/paper/2503.14966/) (2025)
- [AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style](https://mlsanity.com/paper/2103.13722/) (2021)
- [Discriminative Probing and Tuning for Text-to-Image Generation](https://mlsanity.com/paper/2403.04321/) (2024)
- [Dungeon and Platformer Level Blending and Generation using Conditional VAEs](https://mlsanity.com/paper/2106.12692/) (2021)
- [Graphine: A Dataset for Graph-aware Terminology Definition Generation](https://mlsanity.com/paper/2109.04018/) (2021)
- [EmpHi: Generating Empathetic Responses with Human-like Intents](https://mlsanity.com/paper/2204.12191/) (2022)
- [Testing for the Markov Property in Time Series via Deep Conditional Generative Learning](https://mlsanity.com/paper/2305.19244/) (2023)
- [Closed-Loop Unsupervised Representation Disentanglement with β-VAE Distillation and Diffusion Probabilistic Feedback](https://mlsanity.com/paper/2402.02346/) (2024)

## Abstract (arXiv)

This paper investigates a novel problem of generating images from visual attributes. We model the image as a composite of foreground and background and develop a layered generative model with disentangled latent variables that can be learned end-to-end using a variational auto-encoder. We experiment with natural images of faces and birds and demonstrate that the proposed models are capable of generating realistic and diverse samples with disentangled latent representations. We use a general energy minimization algorithm for posterior inference of latent variables given novel images. Therefore, the learned generative models show excellent quantitative and visual results in the tasks of attribute-conditioned image reconstruction and completion.