# Pose-Normalized Image Generation for Person Re-identification

arXiv: https://arxiv.org/abs/1712.02225 · HTML: https://mlsanity.com/paper/1712.02225/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2017.

## Built on by (most cited first, 30 of 70)

- [Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization](https://mlsanity.com/paper/2103.15537/) (2021)
- [NFormer: Robust Person Re-identification with Neighbor Transformer](https://mlsanity.com/paper/2204.09331/) (2022)
- [Learning Semantic Person Image Generation by Region-Adaptive Normalization](https://mlsanity.com/paper/2104.06650/) (2021)
- [Large-Scale Pre-training for Person Re-identification with Noisy Labels](https://mlsanity.com/paper/2203.16533/) (2022)
- [Feature Completion for Occluded Person Re-Identification](https://mlsanity.com/paper/2106.12733/) (2021)
- [Dynamic Prototype Mask for Occluded Person Re-Identification](https://mlsanity.com/paper/2207.09046/) (2022)
- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [CLIP-Driven Semantic Discovery Network for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2401.05806/) (2024)
- [Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking](https://mlsanity.com/paper/2407.13646/) (2024)
- [Semantic-aware Consistency Network for Cloth-changing Person Re-Identification](https://mlsanity.com/paper/2308.14113/) (2023)
- [From Poses to Identity: Training-Free Person Re-Identification via Feature Centralization](https://mlsanity.com/paper/2503.00938/) (2025)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks](https://mlsanity.com/paper/2503.06965/) (2025)
- [Graph-based Person Signature for Person Re-Identifications](https://mlsanity.com/paper/2104.06770/) (2021)
- [The Image Local Autoregressive Transformer](https://mlsanity.com/paper/2106.02514/) (2021)
- [Learning Multi-Attention Context Graph for Group-Based Re-Identification](https://mlsanity.com/paper/2104.14236/) (2021)
- [Person Re-identification via Attention Pyramid](https://mlsanity.com/paper/2108.05340/) (2021)
- [Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification](https://mlsanity.com/paper/2308.10692/) (2023)
- [Pose-driven Attention-guided Image Generation for Person Re-Identification](https://mlsanity.com/paper/2104.13773/) (2021)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-Training](https://mlsanity.com/paper/2406.06045/) (2024)
- [Exploring the Quality of GAN Generated Images for Person Re-Identification](https://mlsanity.com/paper/2108.09977/) (2021)
- [Low-resolution Human Pose Estimation](https://mlsanity.com/paper/2109.09090/) (2021)
- [Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification](https://mlsanity.com/paper/2203.15210/) (2022)
- [Learning Invariance from Generated Variance for Unsupervised Person Re-identification](https://mlsanity.com/paper/2301.00725/) (2023)
- [Whole-body Detection, Recognition and Identification at Altitude and Range](https://mlsanity.com/paper/2311.05725/) (2023)
- [Rethinking Person Re-Identification via Semantic-Based Pretraining](https://mlsanity.com/paper/2110.05074/) (2021)
- [Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning](https://mlsanity.com/paper/2406.12316/) (2024)
- [Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification](https://mlsanity.com/paper/2406.16042/) (2024)
- [OpenAnimals: Revisiting Person Re-Identification for Animals Towards Better Generalization](https://mlsanity.com/paper/2410.00204/) (2024)

## Abstract (arXiv)

Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations. In this work, we address both problems by proposing a novel deep person image generation model for synthesizing realistic person images conditional on the pose. The model is based on a generative adversarial network (GAN) designed specifically for pose normalization in re-id, thus termed pose-normalization GAN (PN-GAN). With the synthesized images, we can learn a new type of deep re-id feature free of the influence of pose variations. We show that this feature is strong on its own and complementary to features learned with the original images. Importantly, under the transfer learning setting, we show that our model generalizes well to any new re-id dataset without the need for collecting any training data for model fine-tuning. The model thus has the potential to make re-id model truly scalable.