# GUAVA: Generalizable Upper Body 3D Gaussian Avatar

arXiv: https://arxiv.org/abs/2505.03351 · HTML: https://mlsanity.com/paper/2505.03351/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 102 of its 117 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 30 of 102)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Variational Diffusion Models](https://mlsanity.com/paper/2107.00630/) (2021)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://mlsanity.com/paper/2309.16653/) (2023)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [VideoGPT: Video Generation using VQ-VAE and Transformers](https://mlsanity.com/paper/2104.10157/) (2021)
- [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://mlsanity.com/paper/2012.15838/) (2020)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models](https://mlsanity.com/paper/2401.09047/) (2024)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video](https://mlsanity.com/paper/2201.04127/) (2022)
- [Learning an Animatable Detailed 3D Face Model from In-The-Wild Images](https://mlsanity.com/paper/2012.04012/) (2020)
- [Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction](https://mlsanity.com/paper/2012.03065/) (2020)

## Built on by (most cited first, 22 of 22)

- [LangSplatV2: High-dimensional 3D Language Gaussian Splatting with 450+ FPS](https://mlsanity.com/paper/2507.07136/) (2025)
- [Forge4D: Feed-Forward 4D Human Reconstruction and Interpolation from Uncalibrated Sparse-view Videos](https://mlsanity.com/paper/2509.24209/) (2025)
- [STAvatar: Soft Binding and Temporal Density Control for Monocular 3D Head Avatars Reconstruction](https://mlsanity.com/paper/2511.19854/) (2025)
- [Bringing Your Portrait to 3D Presence](https://mlsanity.com/paper/2511.22553/) (2025)
- [PEAR: Pixel-aligned Expressive humAn mesh Recovery](https://mlsanity.com/paper/2601.22693/) (2026)
- [Large-scale Codec Avatars: The Unreasonable Effectiveness of Large-scale Avatar Pretraining](https://mlsanity.com/paper/2604.02320/) (2026)
- [SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning](https://mlsanity.com/paper/2603.10446/) (2026)
- [M3T: Discrete Multi-Modal Motion Tokens for Sign Language Production](https://mlsanity.com/paper/2603.23617/) (2026)
- [VisualSpeaker: Visually-Guided 3D Avatar Lip Synthesis](https://mlsanity.com/paper/2507.06060/) (2025)
- [OMG-Avatar: One-shot Multi-LOD Gaussian Head Avatar](https://mlsanity.com/paper/2603.01506/) (2026)
- [BackTranslation2.0 -- A Linguistically Motivated Metric to Assess Sign Language Production](https://mlsanity.com/paper/2606.28673/) (2026)
- [Diverse Signer Avatars with Manual and Non-Manual Feature Modelling for Sign Language Production](https://mlsanity.com/paper/2508.15988/) (2025)
- [FMGS-Avatar: Mesh-Guided 2D Gaussian Splatting with Foundation Model Priors for 3D Monocular Avatar Reconstruction](https://mlsanity.com/paper/2509.14739/) (2025)
- [ViSA: 3D-Aware Video Shading for Real-Time Upper-Body Avatar Creation](https://mlsanity.com/paper/2512.07720/) (2025)
- [LentiAvatar: Pseudo-Multiview Reconstruction and Subpixel Prism Rendering for Real-Time Stereoscopic Communication](https://mlsanity.com/paper/2606.10550/) (2026)
- [Hand-4DGS: Feed-Forward 3D Gaussian Splatting for 4D Hand Reconstruction from Egocentric Videos](https://mlsanity.com/paper/2606.19156/) (2026)
- [SignNet-1M: Large-Scale Multilingual Sign Language Video Dataset with Downstream Benchmarks](https://mlsanity.com/paper/2606.24361/) (2026)
- [EMOSH: Expressive Motion and Shape Disentanglement for Human Animation](https://mlsanity.com/paper/2606.28026/) (2026)
- [FlexiAvatar: Unified 3D Gaussian Human Avatars Under Arbitrary Body Visibility](https://mlsanity.com/paper/2607.19100/) (2026)
- [OASIS: Occlusion-aware Single-image Hand Avatar Reconstruction via 3D Gaussian Splatting](https://mlsanity.com/paper/2607.29633/) (2026)
- [Multi-View Face and Gesture Animation with Dynamic Gaussians](https://mlsanity.com/paper/2608.04722/) (2026)
- [Learning Semantic Inpainting for Animatable Gaussian Head Avatars](https://mlsanity.com/paper/2609.38343/) (2026)

## Abstract (arXiv)

Reconstructing a high-quality, animatable 3D human avatar with expressive facial and hand motions from a single image has gained significant attention due to its broad application potential. 3D human avatar reconstruction typically requires multi-view or monocular videos and training on individual IDs, which is both complex and time-consuming. Furthermore, limited by SMPLX's expressiveness, these methods often focus on body motion but struggle with facial expressions. To address these challenges, we first introduce an expressive human model (EHM) to enhance facial expression capabilities and develop an accurate tracking method. Based on this template model, we propose GUAVA, the first framework for fast animatable upper-body 3D Gaussian avatar reconstruction. We leverage inverse texture mapping and projection sampling techniques to infer Ubody (upper-body) Gaussians from a single image. The rendered images are refined through a neural refiner. Experimental results demonstrate that GUAVA significantly outperforms previous methods in rendering quality and offers significant speed improvements, with reconstruction times in the sub-second range (0.1s), and supports real-time animation and rendering.