# GGAvatar: Reconstructing Garment-Separated 3D Gaussian Splatting Avatars from Monocular Video

arXiv: https://arxiv.org/abs/2411.09952 · HTML: https://mlsanity.com/paper/2411.09952/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 44 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 29 of 29)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis](https://mlsanity.com/paper/2308.09713/) (2023)
- [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://mlsanity.com/paper/2012.15838/) (2020)
- [HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video](https://mlsanity.com/paper/2201.04127/) (2022)
- [STAR: Sparse Trained Articulated Human Body Regressor](https://mlsanity.com/paper/2008.08535/) (2020)
- [GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians](https://mlsanity.com/paper/2312.02134/) (2023)
- [Self-Correction for Human Parsing](https://mlsanity.com/paper/1910.09777/) (2019)
- [FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration](https://mlsanity.com/paper/2108.06428/) (2021)
- [GauHuman: Articulated Gaussian Splatting from Monocular Human Videos](https://mlsanity.com/paper/2312.02973/) (2023)
- [SMPLicit: Topology-aware Generative Model for Clothed People](https://mlsanity.com/paper/2103.06871/) (2021)
- [InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds](https://mlsanity.com/paper/2212.10550/) (2022)
- [BCNet: Learning Body and Cloth Shape from A Single Image](https://mlsanity.com/paper/2004.00214/) (2020)
- [Detailed Human Avatars from Monocular Video](https://mlsanity.com/paper/1808.01338/) (2018)
- [Animatable Neural Radiance Fields from Monocular RGB Videos](https://mlsanity.com/paper/2106.13629/) (2021)
- [Capturing and Animation of Body and Clothing from Monocular Video](https://mlsanity.com/paper/2210.01868/) (2022)
- [Learning Neural Volumetric Representations of Dynamic Humans in Minutes](https://mlsanity.com/paper/2302.12237/) (2023)
- [Modeling Clothing as a Separate Layer for an Animatable Human Avatar](https://mlsanity.com/paper/2106.14879/) (2021)
- [Fast-SNARF: A Fast Deformer for Articulated Neural Fields](https://mlsanity.com/paper/2211.15601/) (2022)
- [PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations](https://mlsanity.com/paper/2404.04421/) (2024)
- [DrapeNet: Garment Generation and Self-Supervised Draping](https://mlsanity.com/paper/2211.11277/) (2022)
- [ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns](https://mlsanity.com/paper/2305.14100/) (2023)
- [DIG: Draping Implicit Garment over the Human Body](https://mlsanity.com/paper/2209.10845/) (2022)
- [S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields](https://mlsanity.com/paper/2308.07032/) (2023)
- [LayGA: Layered Gaussian Avatars for Animatable Clothing Transfer](https://mlsanity.com/paper/2405.07319/) (2024)
- [GALA: Generating Animatable Layered Assets from a Single Scan](https://mlsanity.com/paper/2401.12979/) (2024)

## Built on by (most cited first, 4 of 4)

- [A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation](https://mlsanity.com/paper/2508.09977/) (2025)
- [VGNC: Reducing the Overfitting of Sparse-view 3DGS via Validation-guided Gaussian Number Control](https://mlsanity.com/paper/2504.14548/) (2025)
- [Information-Regularized Constrained Inversion for Stable Avatar Editing from Sparse Supervision](https://mlsanity.com/paper/2604.02883/) (2026)
- [AvatarMix: Identity-Preserving Cross-Avatar Composition for Outfit Personalization](https://mlsanity.com/paper/2606.03506/) (2026)

## Abstract (arXiv)

Avatar modelling has broad applications in human animation and virtual try-ons. Recent advancements in this field have focused on high-quality and comprehensive human reconstruction but often overlook the separation of clothing from the body. To bridge this gap, this paper introduces GGAvatar (Garment-separated 3D Gaussian Splatting Avatar), which relies on monocular videos. Through advanced parameterized templates and unique phased training, this model effectively achieves decoupled, editable, and realistic reconstruction of clothed humans. Comparative evaluations with other costly models confirm GGAvatar's superior quality and efficiency in modelling both clothed humans and separable garments. The paper also showcases applications in clothing editing, as illustrated in Figure 1, highlighting the model's benefits and the advantages of effective disentanglement. The code is available at https://github.com/J-X-Chen/GGAvatar/.