Thin-Plate Spline Motion Model for Image Animation
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Cited by 53 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Image animation brings life to the static object in the source image according to the driving video. Recent works attempt to perform motion transfer on arbitrary objects through unsupervised methods without using a priori knowledge. However, it remains a significant challenge for current unsupervised methods when there is a large pose gap between the objects in the source and driving images. In this paper, a new end-to-end unsupervised motion transfer framework is proposed to overcome such issue. Firstly, we propose thin-plate spline motion estimation to produce a more flexible optical flow, which warps the feature maps of the source image to the feature domain of the driving image. Secondly, in order to restore the missing regions more realistically, we leverage multi-resolution occlusion masks to achieve more effective feature fusion. Finally, additional auxiliary loss functions are designed to ensure that there is a clear division of labor in the network modules, encouraging the network to generate high-quality images. Our method can animate a variety of objects, including talking faces, human bodies, and pixel animations. Experiments demonstrate that our method performs better on most benchmarks than the state of the art with visible improvements in pose-related metrics.
Key facts. Cited by 209 papers in the map (data through 7 Oct 2026). Builds on 31 of its 37 references that are in the map. First posted on arXiv 27 Mar 2022.
Builds on
31 of 37 references.
References · 31
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
- 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
- 2016Pyramid Scene Parsing Networkcited by 2,472
- 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
- 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,061
- 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
- 2017In Defense of the Triplet Loss for Person Re-Identificationcited by 784
- 2017VoxCeleb: a large-scale speaker identification datasetcited by 714
- 2020First Order Motion Model for Image Animationcited by 698
- 2020Face2Face: Real-time Face Capture and Reenactment of RGB Videoscited by 631
- 2017How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)cited by 524
19 more
- 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 452
- 2020One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencingcited by 438
- 2018Everybody Dance Nowcited by 313
- 2019Few-Shot Adversarial Learning of Realistic Neural Talking Head Modelscited by 284
- 2021Motion Representations for Articulated Animationcited by 253
- 2015Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networkscited by 214
- 2018Animating Arbitrary Objects via Deep Motion Transfercited by 206
- 2018X2Face: A network for controlling face generation by using images, audio, and pose codescited by 189
- 2017Deformable GANs for Pose-based Human Image Generationcited by 166
- 2019Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesiscited by 161
- 2018GANimation: Anatomically-aware Facial Animation from a Single Imagecited by 160
- 2019Progressive Pose Attention Transfer for Person Image Generationcited by 138
- 2020HeadGAN: One-shot Neural Head Synthesis and Editingcited by 105
- 2020Deep Image Spatial Transformation for Person Image Generationcited by 100
- 2019MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targetscited by 90
- 2015face anti-spoofing based on color texture analysiscited by 82
- 2021Deep Learning for Face Anti-Spoofing: A Surveycited by 64
- 2021High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Networkcited by 61
- 2017Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognitioncited by 24
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Citing papers per year: 2022: 5; 2023: 37; 2024: 73; 2025: 69; 2026: 25.
209 papers.
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