# MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation

arXiv: https://arxiv.org/abs/1911.11758 · HTML: https://mlsanity.com/paper/1911.11758/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 29 of 29)

- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://mlsanity.com/paper/2301.07093/) (2023)
- [Towards Universal Fake Image Detectors that Generalize Across Generative Models](https://mlsanity.com/paper/2302.10174/) (2023)
- [Counterfactual Generative Networks](https://mlsanity.com/paper/2101.06046/) (2021)
- [GIRAFFE HD: A High-Resolution 3D-aware Generative Model](https://mlsanity.com/paper/2203.14954/) (2022)
- [Picture that Sketch: Photorealistic Image Generation from Abstract Sketches](https://mlsanity.com/paper/2303.11162/) (2023)
- [ComCLIP: Training-Free Compositional Image and Text Matching](https://mlsanity.com/paper/2211.13854/) (2022)
- [Multifactor Sequential Disentanglement via Structured Koopman Autoencoders](https://mlsanity.com/paper/2303.17264/) (2023)
- [Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer](https://mlsanity.com/paper/2108.07520/) (2021)
- [Hallucinating Pose-Compatible Scenes](https://mlsanity.com/paper/2112.06909/) (2021)
- [DR-GAN: Distribution Regularization for Text-to-Image Generation](https://mlsanity.com/paper/2204.07945/) (2022)
- [MOGAN: Morphologic-structure-aware Generative Learning from a Single Image](https://mlsanity.com/paper/2103.02997/) (2021)
- [Contrastive Fine-grained Class Clustering via Generative Adversarial Networks](https://mlsanity.com/paper/2112.14971/) (2021)
- [DisUnknown: Distilling Unknown Factors for Disentanglement Learning](https://mlsanity.com/paper/2109.08090/) (2021)
- [DSVAE: Interpretable Disentangled Representation for Synthetic Speech Detection](https://mlsanity.com/paper/2304.03323/) (2023)
- [GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes](https://mlsanity.com/paper/2106.13215/) (2021)
- [Controllable Image Generation via Collage Representations](https://mlsanity.com/paper/2304.13722/) (2023)
- [Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey](https://mlsanity.com/paper/2111.13282/) (2021)
- [Contrastive Learning for Diverse Disentangled Foreground Generation](https://mlsanity.com/paper/2211.02707/) (2022)
- [α-TCVAE: On the relationship between Disentanglement and Diversity](https://mlsanity.com/paper/2411.00588/) (2024)
- [Generating Furry Cars: Disentangling Object Shape & Appearance across Multiple Domains](https://mlsanity.com/paper/2104.02052/) (2021)
- [CoDeGAN: Contrastive Disentanglement for Generative Adversarial Network](https://mlsanity.com/paper/2103.03636/) (2021)
- [Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification](https://mlsanity.com/paper/2507.21703/) (2025)
- [Unsupervised Structure-Consistent Image-to-Image Translation](https://mlsanity.com/paper/2208.11546/) (2022)
- [Break The Spell Of Total Correlation In betaTCVAE](https://mlsanity.com/paper/2210.08794/) (2022)
- [Chirpy3D: Part-Aware Multi-View Diffusion for Creative Fine-Grained Object Generation](https://mlsanity.com/paper/2501.04144/) (2025)
- [Blocked and Hierarchical Disentangled Representation From Information Theory Perspective](https://mlsanity.com/paper/2101.08408/) (2021)
- [DSFFNet: Dual-Side Feature Fusion Network for 3D Pose Transfer](https://mlsanity.com/paper/2305.14951/) (2023)
- [Content-Aware Preserving Image Generation](https://mlsanity.com/paper/2411.09871/) (2024)
- [Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent](https://mlsanity.com/paper/2606.24094/) (2026)

## Abstract (arXiv)

We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation. We build upon FineGAN, an unconditional generative model, to learn the desired disentanglement and image generator, and leverage adversarial joint image-code distribution matching to learn the latent factor encoders. MixNMatch requires bounding boxes during training to model background, but requires no other supervision. Through extensive experiments, we demonstrate MixNMatch's ability to accurately disentangle, encode, and combine multiple factors for mix-and-match image generation, including sketch2color, cartoon2img, and img2gif applications. Our code/models/demo can be found at https://github.com/Yuheng-Li/MixNMatch