# LAM: Large Avatar Model for One-shot Animatable Gaussian Head

arXiv: https://arxiv.org/abs/2502.17796 · HTML: https://mlsanity.com/paper/2502.17796/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 66 of its 85 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 30 of 66)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [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)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation](https://mlsanity.com/paper/2402.05054/) (2024)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [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)
- [Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions](https://mlsanity.com/paper/2303.12789/) (2023)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video](https://mlsanity.com/paper/2012.12247/) (2020)
- [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)
- [SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation](https://mlsanity.com/paper/2211.12194/) (2022)
- [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)
- [GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians](https://mlsanity.com/paper/2312.02069/) (2023)
- [EMO: Emote Portrait Alive -- Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions](https://mlsanity.com/paper/2402.17485/) (2024)
- [HeadNeRF: A Real-time NeRF-based Parametric Head Model](https://mlsanity.com/paper/2112.05637/) (2021)
- [HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields](https://mlsanity.com/paper/2106.13228/) (2021)
- [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://mlsanity.com/paper/2407.03168/) (2024)
- [PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering](https://mlsanity.com/paper/2109.08379/) (2021)
- [Hallo: Hierarchical Audio-Driven Visual Synthesis for Portrait Image Animation](https://mlsanity.com/paper/2406.08801/) (2024)
- [PointAvatar: Deformable Point-based Head Avatars from Videos](https://mlsanity.com/paper/2212.08377/) (2022)
- [Depth-Aware Generative Adversarial Network for Talking Head Video Generation](https://mlsanity.com/paper/2203.06605/) (2022)
- [VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution](https://mlsanity.com/paper/2205.03409/) (2022)

## Built on by (most cited first, 30 of 43)

- [SEGA: Drivable 3D Gaussian Head Avatar from a Single Image](https://mlsanity.com/paper/2504.14373/) (2025)
- [FastAvatar: Towards Unified and Fast 3D Avatar Reconstruction with Large Gaussian Reconstruction Transformers](https://mlsanity.com/paper/2508.19754/) (2025)
- [PanoLAM: Large Avatar Model for Gaussian Full-Head Synthesis from One-shot Unposed Image](https://mlsanity.com/paper/2509.07552/) (2025)
- [Forge4D: Feed-Forward 4D Human Reconstruction and Interpolation from Uncalibrated Sparse-view Videos](https://mlsanity.com/paper/2509.24209/) (2025)
- [FastGHA: Generalized Few-Shot 3D Gaussian Head Avatars with Real-Time Animation](https://mlsanity.com/paper/2601.13837/) (2026)
- [Bringing Your Portrait to 3D Presence](https://mlsanity.com/paper/2511.22553/) (2025)
- [AGORA: Adversarial Generation Of Real-time Animatable 3D Gaussian Head Avatars](https://mlsanity.com/paper/2512.06438/) (2025)
- [VASA-3D: Lifelike Audio-Driven Gaussian Head Avatars from a Single Image](https://mlsanity.com/paper/2512.14677/) (2025)
- [Towards Seamless Interaction: Causal Turn-Level Modeling of Interactive 3D Conversational Head Dynamics](https://mlsanity.com/paper/2512.15340/) (2025)
- [Snapmoji: Instant Generation of Animatable Dual-Stylized Avatars](https://mlsanity.com/paper/2503.11978/) (2025)
- [SemanticFace: Semantic Facial Action Estimation via Semantic Distillation in Interpretable Space](https://mlsanity.com/paper/2603.14827/) (2026)
- [ArchitectHead: Continuous Level of Detail Control for 3D Gaussian Head Avatars](https://mlsanity.com/paper/2510.05488/) (2025)
- [VRGaussianAvatar: Integrating 3D Gaussian Avatars into VR](https://mlsanity.com/paper/2602.01674/) (2026)
- [From Blurry to Believable: Enhancing Low-quality Talking Heads with 3D Generative Priors](https://mlsanity.com/paper/2602.06122/) (2026)
- [AvatarPointillist: AutoRegressive 4D Gaussian Avatarization](https://mlsanity.com/paper/2604.04787/) (2026)
- [Any3DAvatar: Fast and High-Quality Full-Head 3D Avatar Reconstruction from Single Portrait Image](https://mlsanity.com/paper/2604.13856/) (2026)
- [Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multi-View Captures](https://mlsanity.com/paper/2605.04035/) (2026)
- [FFAvatar: Few-Shot, Feed-Forward, and Generalizable Avatar Reconstruction](https://mlsanity.com/paper/2605.15320/) (2026)
- [Low-Rank Head Avatar Personalization with Registers](https://mlsanity.com/paper/2506.01935/) (2025)
- [Reconstruction and Reenactment Separated Method for Realistic Gaussian Head](https://mlsanity.com/paper/2509.05582/) (2025)
- [Instant Expressive Gaussian Head Avatars at Over 100 FPS](https://mlsanity.com/paper/2512.16893/) (2025)
- [One-Shot Feed-Forward 360circ Animatable Avatar via Inpainted UV-Space Gaussian Modeling](https://mlsanity.com/paper/2601.12770/) (2026)
- [OMEGA-Avatar: One-shot Modeling of 360deg Gaussian Avatars](https://mlsanity.com/paper/2602.11693/) (2026)
- [LiftAvatar: Kinematic-Space Completion for Expression-Controlled 3D Gaussian Avatar Animation](https://mlsanity.com/paper/2603.02129/) (2026)
- [Retrieval-Augmented Gaussian Avatars: Improving Expression Generalization](https://mlsanity.com/paper/2603.08645/) (2026)
- [3DRealHead: Few-Shot Detailed Head Avatar](https://mlsanity.com/paper/2604.13171/) (2026)
- [Giving Faces Their Feelings Back: Explicit Emotion Control for Feedforward Single-Image 3D Head Avatars](https://mlsanity.com/paper/2604.14541/) (2026)
- [One-shot Compositional 3D Head Avatars with Deformable Hair](https://mlsanity.com/paper/2604.14782/) (2026)
- [AudioFace: Language-Assisted Speech-Driven Facial Animation with Multimodal Language Models](https://mlsanity.com/paper/2605.07478/) (2026)
- [SplitAvatar: One-shot Head Avatar with Autoregressive Gaussian Splitting](https://mlsanity.com/paper/2605.25751/) (2026)

## Abstract (arXiv)

We present LAM, an innovative Large Avatar Model for animatable Gaussian head reconstruction from a single image. Unlike previous methods that require extensive training on captured video sequences or rely on auxiliary neural networks for animation and rendering during inference, our approach generates Gaussian heads that are immediately animatable and renderable. Specifically, LAM creates an animatable Gaussian head in a single forward pass, enabling reenactment and rendering without additional networks or post-processing steps. This capability allows for seamless integration into existing rendering pipelines, ensuring real-time animation and rendering across a wide range of platforms, including mobile phones. The centerpiece of our framework is the canonical Gaussian attributes generator, which utilizes FLAME canonical points as queries. These points interact with multi-scale image features through a Transformer to accurately predict Gaussian attributes in the canonical space. The reconstructed canonical Gaussian avatar can then be animated utilizing standard linear blend skinning (LBS) with corrective blendshapes as the FLAME model did and rendered in real-time on various platforms. Our experimental results demonstrate that LAM outperforms state-of-the-art methods on existing benchmarks. Our code and video are available at https://aigc3d.github.io/projects/LAM/