# MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation

arXiv: https://arxiv.org/abs/2011.09084 · HTML: https://mlsanity.com/paper/2011.09084/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 16 of 16)

- [HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation](https://mlsanity.com/paper/2304.04269/) (2023)
- [A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation](https://mlsanity.com/paper/2304.02858/) (2023)
- [GRPose: Learning Graph Relations for Human Image Generation with Pose Priors](https://mlsanity.com/paper/2408.16540/) (2024)
- [UniHuman: A Unified Model for Editing Human Images in the Wild](https://mlsanity.com/paper/2312.14985/) (2023)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [Semantic 3D-aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field](https://mlsanity.com/paper/2302.01579/) (2023)
- [Self-supervised Correlation Mining Network for Person Image Generation](https://mlsanity.com/paper/2111.13307/) (2021)
- [BeyondScene: Higher-Resolution Human-Centric Scene Generation With Pretrained Diffusion](https://mlsanity.com/paper/2404.04544/) (2024)
- [Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Textures](https://mlsanity.com/paper/2210.01887/) (2022)
- [CFFT-GAN: Cross-domain Feature Fusion Transformer for Exemplar-based Image Translation](https://mlsanity.com/paper/2302.01608/) (2023)
- [A Unified Framework for Biphasic Facial Age Translation with Noisy-Semantic Guided Generative Adversarial Networks](https://mlsanity.com/paper/2109.07373/) (2021)
- [Single Stage Multi-Pose Virtual Try-On](https://mlsanity.com/paper/2211.10715/) (2022)
- [KB-DMGen: Knowledge-Based Global Guidance and Dynamic Pose Masking for Human Image Generation](https://mlsanity.com/paper/2507.20083/) (2025)
- [Towards Using Clothes Style Transfer for Scenario-aware Person Video Generation](https://mlsanity.com/paper/2110.11894/) (2021)
- [LookinGoodπ: Real-time Person-independent Neural Re-rendering for High-quality Human Performance Capture](https://mlsanity.com/paper/2112.08037/) (2021)
- [DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images](https://mlsanity.com/paper/2412.18797/) (2024)

## Abstract (arXiv)

Pose-guided person image generation usually involves using paired source-target images to supervise the training, which significantly increases the data preparation effort and limits the application of the models. To deal with this problem, we propose a novel multi-level statistics transfer model, which disentangles and transfers multi-level appearance features from person images and merges them with pose features to reconstruct the source person images themselves. So that the source images can be used as supervision for self-driven person image generation. Specifically, our model extracts multi-level features from the appearance encoder and learns the optimal appearance representation through attention mechanism and attributes statistics. Then we transfer them to a pose-guided generator for re-fusion of appearance and pose. Our approach allows for flexible manipulation of person appearance and pose properties to perform pose transfer and clothes style transfer tasks. Experimental results on the DeepFashion dataset demonstrate our method's superiority compared with state-of-the-art supervised and unsupervised methods. In addition, our approach also performs well in the wild.