# AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment

arXiv: https://arxiv.org/abs/2111.07640 · HTML: https://mlsanity.com/paper/2111.07640/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 39 references that are in the map.
- First posted on arXiv 15 Nov 2021.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction](https://mlsanity.com/paper/2012.03065/) (2020)
- [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020)
- [AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis](https://mlsanity.com/paper/2103.11078/) (2021)
- [Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://mlsanity.com/paper/1905.08233/) (2019)
- [Few-shot Image Generation via Cross-domain Correspondence](https://mlsanity.com/paper/2104.06820/) (2021)
- [PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering](https://mlsanity.com/paper/2109.08379/) (2021)
- [Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars](https://mlsanity.com/paper/2008.10174/) (2020)
- [Neural Head Reenactment with Latent Pose Descriptors](https://mlsanity.com/paper/2004.12000/) (2020)
- [MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets](https://mlsanity.com/paper/1911.08139/) (2019)
- [FACIAL: Synthesizing Dynamic Talking Face with Implicit Attribute Learning](https://mlsanity.com/paper/2108.07938/) (2021)
- [Deep Image Blending](https://mlsanity.com/paper/1910.11495/) (2019)
- [Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence](https://mlsanity.com/paper/2005.05207/) (2020)
- [Cross-Domain and Disentangled Face Manipulation with 3D Guidance](https://mlsanity.com/paper/2104.11228/) (2021)
- [DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition](https://mlsanity.com/paper/2101.08674/) (2021)
- [ACFD: Asymmetric Cartoon Face Detector](https://mlsanity.com/paper/2007.00899/) (2020)

## Built on by (most cited first, 11 of 11)

- [Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes](https://mlsanity.com/paper/2303.02760/) (2023)
- [PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters](https://mlsanity.com/paper/2303.14587/) (2023)
- [Deep Geometrized Cartoon Line Inbetweening](https://mlsanity.com/paper/2309.16643/) (2023)
- [One missing piece in Vision and Language: A Survey on Comics Understanding](https://mlsanity.com/paper/2409.09502/) (2024)
- [AnimeShooter: A Multi-Shot Animation Dataset for Reference-Guided Video Generation](https://mlsanity.com/paper/2506.03126/) (2025)
- [AnimeGamer: Infinite Anime Life Simulation with Next Game State Prediction](https://mlsanity.com/paper/2504.01014/) (2025)
- [A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation](https://mlsanity.com/paper/2308.08849/) (2023)
- [AniArtAvatar: Animatable 3D Art Avatar from a Single Image](https://mlsanity.com/paper/2403.17631/) (2024)
- [Expression Domain Translation Network for Cross-domain Head Reenactment](https://mlsanity.com/paper/2310.10073/) (2023)
- [Talking Head(?) Anime from a Single Image 4: Improved Model and Its Distillation](https://mlsanity.com/paper/2311.17409/) (2023)
- [OregairuChar: A Benchmark Dataset for Character Appearance Frequency Analysis in My Teen Romantic Comedy SNAFU](https://mlsanity.com/paper/2511.05263/) (2025)

## Abstract (arXiv)

We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment. Different from previous animation head datasets, we utilize 3D animation models as the controllable image samplers, which can provide a large amount of head images with their corresponding detailed pose annotations. To facilitate a data creation process, we build a semi-automatic pipeline leveraging an open 3D computer graphics software with a developed annotation system. After training with the AnimeCeleb, recent head reenactment models produce high-quality animation head reenactment results, which are not achievable with existing datasets. Furthermore, motivated by metaverse application, we propose a novel pose mapping method and architecture to tackle a cross-domain head reenactment task. During inference, a user can easily transfer one's motion to an arbitrary animation head. Experiments demonstrate the usefulness of the AnimeCeleb to train animation head reenactment models, and the superiority of our cross-domain head reenactment model compared to state-of-the-art methods. Our dataset and code are available at https://github.com/kangyeolk/AnimeCeleb.