# Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis

arXiv: https://arxiv.org/abs/2402.18078 · HTML: https://mlsanity.com/paper/2402.18078/

## Key facts

- Cited by 48 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 51 of its 64 references that are in the map.
- First posted on arXiv 28 Feb 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, 30 of 51)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model](https://mlsanity.com/paper/2311.16498/) (2023)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)

## Built on by (most cited first, 30 of 48)

- [Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis](https://mlsanity.com/paper/2404.13686/) (2024)
- [Controllable Generation with Text-to-Image Diffusion Models: A Survey](https://mlsanity.com/paper/2403.04279/) (2024)
- [Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis](https://mlsanity.com/paper/2507.18569/) (2025)
- [Conditional Image Synthesis with Diffusion Models: A Survey](https://mlsanity.com/paper/2409.19365/) (2024)
- [Learning Flow Fields in Attention for Controllable Person Image Generation](https://mlsanity.com/paper/2412.08486/) (2024)
- [Group Diffusion Transformers are Unsupervised Multitask Learners](https://mlsanity.com/paper/2410.15027/) (2024)
- [AnyTalker: Scaling Multi-Person Talking Video Generation with Interactivity Refinement](https://mlsanity.com/paper/2511.23475/) (2025)
- [MagicTryOn: Harnessing Diffusion Transformer for Garment-Preserving Video Virtual Try-on](https://mlsanity.com/paper/2505.21325/) (2025)
- [ByteEdit: Boost, Comply and Accelerate Generative Image Editing](https://mlsanity.com/paper/2404.04860/) (2024)
- [Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation](https://mlsanity.com/paper/2408.16506/) (2024)
- [MS-DETR: Towards Effective Video Moment Retrieval and Highlight Detection by Joint Motion-Semantic Learning](https://mlsanity.com/paper/2507.12062/) (2025)
- [EgoWorld: Translating Exocentric View to Egocentric View using Rich Exocentric Observations](https://mlsanity.com/paper/2506.17896/) (2025)
- [One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer](https://mlsanity.com/paper/2511.22940/) (2025)
- [SlerpFace: Face Template Protection via Spherical Linear Interpolation](https://mlsanity.com/paper/2407.03043/) (2024)
- [Fine-Grained Zero-Shot Object Detection](https://mlsanity.com/paper/2507.10358/) (2025)
- [SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2504.09549/) (2025)
- [Multi-focal Conditioned Latent Diffusion for Person Image Synthesis](https://mlsanity.com/paper/2503.15686/) (2025)
- [AudioScenic: Audio-Driven Video Scene Editing](https://mlsanity.com/paper/2404.16581/) (2024)
- [IGR: Improving Diffusion Model for Garment Restoration from Person Image](https://mlsanity.com/paper/2412.11513/) (2024)
- [ROPA: Synthetic Robot Pose Generation for RGB-D Bimanual Data Augmentation](https://mlsanity.com/paper/2509.19454/) (2025)
- [One-Shot Learning for Pose-Guided Person Image Synthesis in the Wild](https://mlsanity.com/paper/2409.09593/) (2024)
- [VRMDiff: Text-Guided Video Referring Matting Generation of Diffusion](https://mlsanity.com/paper/2503.10678/) (2025)
- [OrthoTryOn: Geometric Orthogonalization for Conflict-Free Unified Fashion Generation](https://mlsanity.com/paper/2606.27880/) (2026)
- [Fine-Grained Human Pose Editing Assessment via Layer-Selective MLLMs](https://mlsanity.com/paper/2601.10369/) (2026)
- [Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization](https://mlsanity.com/paper/2606.04797/) (2026)
- [DAC-Pose: Dual-Agent Collaborative Framework for Pose-Guided Human Generation](https://mlsanity.com/paper/2608.04622/) (2026)
- [Fusion Embedding for Pose-Guided Person Image Synthesis with Diffusion Model](https://mlsanity.com/paper/2412.07333/) (2024)
- [DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images](https://mlsanity.com/paper/2412.18797/) (2024)
- [ODPG: Outfitting Diffusion with Pose Guided Condition](https://mlsanity.com/paper/2501.06769/) (2025)
- [Fine-Grained Controllable Apparel Showcase Image Generation via Garment-Centric Outpainting](https://mlsanity.com/paper/2503.01294/) (2025)

## Abstract (arXiv)

Diffusion model is a promising approach to image generation and has been employed for Pose-Guided Person Image Synthesis (PGPIS) with competitive performance. While existing methods simply align the person appearance to the target pose, they are prone to overfitting due to the lack of a high-level semantic understanding on the source person image. In this paper, we propose a novel Coarse-to-Fine Latent Diffusion (CFLD) method for PGPIS. In the absence of image-caption pairs and textual prompts, we develop a novel training paradigm purely based on images to control the generation process of a pre-trained text-to-image diffusion model. A perception-refined decoder is designed to progressively refine a set of learnable queries and extract semantic understanding of person images as a coarse-grained prompt. This allows for the decoupling of fine-grained appearance and pose information controls at different stages, and thus circumventing the potential overfitting problem. To generate more realistic texture details, a hybrid-granularity attention module is proposed to encode multi-scale fine-grained appearance features as bias terms to augment the coarse-grained prompt. Both quantitative and qualitative experimental results on the DeepFashion benchmark demonstrate the superiority of our method over the state of the arts for PGPIS. Code is available at https://github.com/YanzuoLu/CFLD.