# GRPose: Learning Graph Relations for Human Image Generation with Pose Priors

arXiv: https://arxiv.org/abs/2408.16540 · HTML: https://mlsanity.com/paper/2408.16540/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [Vision GNN: An Image is Worth Graph of Nodes](https://mlsanity.com/paper/2206.00272/) (2022)
- [Controllable Person Image Synthesis with Attribute-Decomposed GAN](https://mlsanity.com/paper/2003.12267/) (2020)
- [HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation](https://mlsanity.com/paper/2304.04269/) (2023)
- [Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models](https://mlsanity.com/paper/2310.06313/) (2023)
- [Exploring Dual-task Correlation for Pose Guided Person Image Generation](https://mlsanity.com/paper/2203.02910/) (2022)
- [Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes](https://mlsanity.com/paper/2303.02760/) (2023)
- [VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis](https://mlsanity.com/paper/2403.08764/) (2024)
- [Learning Semantic Person Image Generation by Region-Adaptive Normalization](https://mlsanity.com/paper/2104.06650/) (2021)
- [EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer](https://mlsanity.com/paper/2312.04152/) (2023)
- [Hyper-3DG: Text-to-3D Gaussian Generation via Hypergraph](https://mlsanity.com/paper/2403.09236/) (2024)
- [Towards Fine-grained Human Pose Transfer with Detail Replenishing Network](https://mlsanity.com/paper/2005.12494/) (2020)
- [MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation](https://mlsanity.com/paper/2011.09084/) (2020)
- [KPE: Keypoint Pose Encoding for Transformer-based Image Generation](https://mlsanity.com/paper/2203.04907/) (2022)
- [TrAME: Trajectory-Anchored Multi-View Editing for Text-Guided 3D Gaussian Splatting Manipulation](https://mlsanity.com/paper/2407.02034/) (2024)

## Built on by (most cited first, 14 of 14)

- [Exploiting Ensemble Learning for Cross-View Isolated Sign Language Recognition](https://mlsanity.com/paper/2502.02196/) (2025)
- [Bidirectional Prototype-Reward co-Evolution for Test-Time Adaptation of Vision-Language Models](https://mlsanity.com/paper/2503.09394/) (2025)
- [Rethink Sparse Signals for Pose-guided Text-to-image Generation](https://mlsanity.com/paper/2506.20983/) (2025)
- [MUSE: A Multi-agent Framework for Unconstrained Story Envisioning via Closed-Loop Cognitive Orchestration](https://mlsanity.com/paper/2602.03028/) (2026)
- [Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignment](https://mlsanity.com/paper/2508.01602/) (2025)
- [EverybodyDance: Bipartite Graph-Based Identity Correspondence for Multi-Character Animation](https://mlsanity.com/paper/2512.16360/) (2025)
- [WorldEdit: Towards Open-World Image Editing with a Knowledge-Informed Benchmark](https://mlsanity.com/paper/2602.07095/) (2026)
- [GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation](https://mlsanity.com/paper/2409.11689/) (2024)
- [KB-DMGen: Knowledge-Based Global Guidance and Dynamic Pose Masking for Human Image Generation](https://mlsanity.com/paper/2507.20083/) (2025)
- [GAOT: Generating Articulated Objects Through Text-Guided Diffusion Models](https://mlsanity.com/paper/2512.03566/) (2025)
- [RiO-DETR: DETR for Real-time Oriented Object Detection](https://mlsanity.com/paper/2603.09411/) (2026)
- [TrioPose: Native Triple-Stream Diffusion Transformers for Pose-Guided Text-to-Image Generation](https://mlsanity.com/paper/2606.07053/) (2026)
- [Jointly Conditioned Diffusion Model for Multi-View Pose-Guided Person Image Synthesis](https://mlsanity.com/paper/2511.15092/) (2025)
- [Astrolabe: Spherical-Map Guidance Across Diffusion Pipelines for Full-Body Capture from Unconstrained Images](https://mlsanity.com/paper/2608.01276/) (2026)

## Abstract (arXiv)

Recent methods using diffusion models have made significant progress in human image generation with various control signals such as pose priors. However, existing efforts are still struggling to generate high-quality images with consistent pose alignment, resulting in unsatisfactory output. In this paper, we propose a framework that delves into the graph relations of pose priors to provide control information for human image generation. The main idea is to establish a graph topological structure between the pose priors and latent representation of diffusion models to capture the intrinsic associations between different pose parts. A Progressive Graph Integrator (PGI) is designed to learn the spatial relationships of the pose priors with the graph structure, adopting a hierarchical strategy within an Adapter to gradually propagate information across different pose parts. Besides, a pose perception loss is introduced based on a pretrained pose estimation network to minimize the pose differences. Extensive qualitative and quantitative experiments conducted on the Human-Art and LAION-Human datasets clearly demonstrate that our model can achieve significant performance improvement over the latest benchmark models. The code is available at urlhttps://xiangchenyin.github.io/GRPose/.