# Deep Spatial Transformation for Pose-Guided Person Image Generation and Animation

arXiv: https://arxiv.org/abs/2008.12606 · HTML: https://mlsanity.com/paper/2008.12606/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 30 of 43)

- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance](https://mlsanity.com/paper/2403.14781/) (2024)
- [PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering](https://mlsanity.com/paper/2109.08379/) (2021)
- [HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation](https://mlsanity.com/paper/2407.17438/) (2024)
- [PISE: Person Image Synthesis and Editing with Decoupled GAN](https://mlsanity.com/paper/2103.04023/) (2021)
- [Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance](https://mlsanity.com/paper/2502.06145/) (2025)
- [HumanDiT: Pose-Guided Diffusion Transformer for Long-form Human Motion Video Generation](https://mlsanity.com/paper/2502.04847/) (2025)
- [VividPose: Advancing Stable Video Diffusion for Realistic Human Image Animation](https://mlsanity.com/paper/2405.18156/) (2024)
- [Bidirectionally Deformable Motion Modulation For Video-based Human Pose Transfer](https://mlsanity.com/paper/2307.07754/) (2023)
- [Dressing in the Wild by Watching Dance Videos](https://mlsanity.com/paper/2203.15320/) (2022)
- [TALK-Act: Enhance Textural-Awareness for 2D Speaking Avatar Reenactment with Diffusion Model](https://mlsanity.com/paper/2410.10696/) (2024)
- [DreamDance: Animating Human Images by Enriching 3D Geometry Cues from 2D Poses](https://mlsanity.com/paper/2412.00397/) (2024)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [Dance Any Beat: Blending Beats with Visuals in Dance Video Generation](https://mlsanity.com/paper/2405.09266/) (2024)
- [Copy Motion From One to Another: Fake Motion Video Generation](https://mlsanity.com/paper/2205.01373/) (2022)
- [Multi-identity Human Image Animation with Structural Video Diffusion](https://mlsanity.com/paper/2504.04126/) (2025)
- [Flow Guided Transformable Bottleneck Networks for Motion Retargeting](https://mlsanity.com/paper/2106.07771/) (2021)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [Zero-shot High-fidelity and Pose-controllable Character Animation](https://mlsanity.com/paper/2404.13680/) (2024)
- [DreamCinema: Cinematic Transfer with Free Camera and 3D Character](https://mlsanity.com/paper/2408.12601/) (2024)
- [HumanRAM: Feed-forward Human Reconstruction and Animation Model using Transformers](https://mlsanity.com/paper/2506.03118/) (2025)
- [Follow-Your-MultiPose: Tuning-Free Multi-Character Text-to-Video Generation via Pose Guidance](https://mlsanity.com/paper/2412.16495/) (2024)
- [HumanGif: Single-View Human Diffusion with Generative Prior](https://mlsanity.com/paper/2502.12080/) (2025)
- [FDA-GAN: Flow-based Dual Attention GAN for Human Pose Transfer](https://mlsanity.com/paper/2112.00281/) (2021)
- [Total Selfie: Generating Full-Body Selfies](https://mlsanity.com/paper/2308.14740/) (2023)
- [MagicPortrait: Temporally Consistent Face Reenactment with 3D Geometric Guidance](https://mlsanity.com/paper/2504.21497/) (2025)
- [MagicFight: Personalized Martial Arts Combat Video Generation](https://mlsanity.com/paper/2601.02107/) (2026)
- [Cloth2Body: Generating 3D Human Body Mesh from 2D Clothing](https://mlsanity.com/paper/2309.16189/) (2023)
- [What Are You Doing? A Closer Look at Controllable Human Video Generation](https://mlsanity.com/paper/2503.04666/) (2025)
- [A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer](https://mlsanity.com/paper/2109.11719/) (2021)

## Abstract (arXiv)

Pose-guided person image generation and animation aim to transform a source person image to target poses. These tasks require spatial manipulation of source data. However, Convolutional Neural Networks are limited by the lack of ability to spatially transform the inputs. In this paper, we propose a differentiable global-flow local-attention framework to reassemble the inputs at the feature level. This framework first estimates global flow fields between sources and targets. Then, corresponding local source feature patches are sampled with content-aware local attention coefficients. We show that our framework can spatially transform the inputs in an efficient manner. Meanwhile, we further model the temporal consistency for the person image animation task to generate coherent videos. The experiment results of both image generation and animation tasks demonstrate the superiority of our model. Besides, additional results of novel view synthesis and face image animation show that our model is applicable to other tasks requiring spatial transformation. The source code of our project is available at https://github.com/RenYurui/Global-Flow-Local-Attention.