# DwNet: Dense warp-based network for pose-guided human video generation

arXiv: https://arxiv.org/abs/1910.09139 · HTML: https://mlsanity.com/paper/1910.09139/

## Key facts

- Cited by 115 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 30 of 115)

- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion](https://mlsanity.com/paper/2304.06025/) (2023)
- [GART: Gaussian Articulated Template Models](https://mlsanity.com/paper/2311.16099/) (2023)
- [EVA3D: Compositional 3D Human Generation from 2D Image Collections](https://mlsanity.com/paper/2210.04888/) (2022)
- [UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animation](https://mlsanity.com/paper/2406.01188/) (2024)
- [Animate-X: Universal Character Image Animation with Enhanced Motion Representation](https://mlsanity.com/paper/2410.10306/) (2024)
- [Single Stage Virtual Try-on via Deformable Attention Flows](https://mlsanity.com/paper/2207.09161/) (2022)
- [HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation](https://mlsanity.com/paper/2407.17438/) (2024)
- [StylePeople: A Generative Model of Fullbody Human Avatars](https://mlsanity.com/paper/2104.08363/) (2021)
- [Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance](https://mlsanity.com/paper/2502.06145/) (2025)
- [Neural Re-Rendering of Humans from a Single Image](https://mlsanity.com/paper/2101.04104/) (2021)
- [AvatarGen: A 3D Generative Model for Animatable Human Avatars](https://mlsanity.com/paper/2211.14589/) (2022)
- [ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation](https://mlsanity.com/paper/2308.00906/) (2023)
- [AG3D: Learning to Generate 3D Avatars from 2D Image Collections](https://mlsanity.com/paper/2305.02312/) (2023)
- [I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models](https://mlsanity.com/paper/2405.16537/) (2024)
- [MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human Captures](https://mlsanity.com/paper/2312.02963/) (2023)
- [Style and Pose Control for Image Synthesis of Humans from a Single Monocular View](https://mlsanity.com/paper/2102.11263/) (2021)
- [Text2Performer: Text-Driven Human Video Generation](https://mlsanity.com/paper/2304.08483/) (2023)
- [HumanGAN: A Generative Model of Humans Images](https://mlsanity.com/paper/2103.06902/) (2021)
- [Human-3Diffusion: Realistic Avatar Creation via Explicit 3D Consistent Diffusion Models](https://mlsanity.com/paper/2406.08475/) (2024)
- [Structure-Aware Motion Transfer with Deformable Anchor Model](https://mlsanity.com/paper/2204.05018/) (2022)
- [HumanGen: Generating Human Radiance Fields with Explicit Priors](https://mlsanity.com/paper/2212.05321/) (2022)
- [Analogist: Out-of-the-box Visual In-Context Learning with Image Diffusion Model](https://mlsanity.com/paper/2405.10316/) (2024)
- [RealisDance-DiT: Simple yet Strong Baseline towards Controllable Character Animation in the Wild](https://mlsanity.com/paper/2504.14977/) (2025)
- [Dynamic Neural Garments](https://mlsanity.com/paper/2102.11811/) (2021)
- [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)
- [A Comprehensive Survey on Human Video Generation: Challenges, Methods, and Insights](https://mlsanity.com/paper/2407.08428/) (2024)
- [Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality](https://mlsanity.com/paper/2410.05203/) (2024)
- [XAGen: 3D Expressive Human Avatars Generation](https://mlsanity.com/paper/2311.13574/) (2023)

## Abstract (arXiv)

Generation of realistic high-resolution videos of human subjects is a challenging and important task in computer vision. In this paper, we focus on human motion transfer - generation of a video depicting a particular subject, observed in a single image, performing a series of motions exemplified by an auxiliary (driving) video. Our GAN-based architecture, DwNet, leverages dense intermediate pose-guided representation and refinement process to warp the required subject appearance, in the form of the texture, from a source image into a desired pose. Temporal consistency is maintained by further conditioning the decoding process within a GAN on the previously generated frame. In this way a video is generated in an iterative and recurrent fashion. We illustrate the efficacy of our approach by showing state-of-the-art quantitative and qualitative performance on two benchmark datasets: TaiChi and Fashion Modeling. The latter is collected by us and will be made publicly available to the community.