# Fast-SNARF: A Fast Deformer for Articulated Neural Fields

arXiv: https://arxiv.org/abs/2211.15601 · HTML: https://mlsanity.com/paper/2211.15601/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 56 of its 76 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 30 of 56)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations](https://mlsanity.com/paper/1906.01618/) (2019)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Convolutional Occupancy Networks](https://mlsanity.com/paper/2003.04618/) (2020)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision](https://mlsanity.com/paper/1912.07372/) (2019)
- [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (2021)
- [PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows](https://mlsanity.com/paper/1906.12320/) (2019)
- [HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video](https://mlsanity.com/paper/2201.04127/) (2022)
- [Baking Neural Radiance Fields for Real-Time View Synthesis](https://mlsanity.com/paper/2103.14645/) (2021)
- [Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes](https://mlsanity.com/paper/2101.10994/) (2021)
- [SAL: Sign Agnostic Learning of Shapes from Raw Data](https://mlsanity.com/paper/1911.10414/) (2019)
- [Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies](https://mlsanity.com/paper/2105.02872/) (2021)
- [Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion](https://mlsanity.com/paper/2003.01456/) (2020)
- [3D Menagerie: Modeling the 3D shape and pose of animals](https://mlsanity.com/paper/1611.07700/) (2016)
- [ICON: Implicit Clothed humans Obtained from Normals](https://mlsanity.com/paper/2112.09127/) (2021)
- [ARCH: Animatable Reconstruction of Clothed Humans](https://mlsanity.com/paper/2004.04572/) (2020)
- [Learning Gradient Fields for Shape Generation](https://mlsanity.com/paper/2008.06520/) (2020)
- [NeuMan: Neural Human Radiance Field from a Single Video](https://mlsanity.com/paper/2203.12575/) (2022)

## Built on by (most cited first, 30 of 55)

- [3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting](https://mlsanity.com/paper/2312.09228/) (2023)
- [SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting](https://mlsanity.com/paper/2403.05087/) (2024)
- [InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds](https://mlsanity.com/paper/2212.10550/) (2022)
- [GART: Gaussian Articulated Template Models](https://mlsanity.com/paper/2311.16099/) (2023)
- [Hi4D: 4D Instance Segmentation of Close Human Interaction](https://mlsanity.com/paper/2303.15380/) (2023)
- [HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video](https://mlsanity.com/paper/2311.18448/) (2023)
- [X-Avatar: Expressive Human Avatars](https://mlsanity.com/paper/2303.04805/) (2023)
- [AG3D: Learning to Generate 3D Avatars from 2D Image Collections](https://mlsanity.com/paper/2305.02312/) (2023)
- [IDOL: Instant Photorealistic 3D Human Creation from a Single Image](https://mlsanity.com/paper/2412.14963/) (2024)
- [Learning Locally Editable Virtual Humans](https://mlsanity.com/paper/2305.00121/) (2023)
- [GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh](https://mlsanity.com/paper/2404.07991/) (2024)
- [NPGA: Neural Parametric Gaussian Avatars](https://mlsanity.com/paper/2405.19331/) (2024)
- [GaussianBody: Clothed Human Reconstruction via 3d Gaussian Splatting](https://mlsanity.com/paper/2401.09720/) (2024)
- [MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos](https://mlsanity.com/paper/2407.08414/) (2024)
- [Human101: Training 100+FPS Human Gaussians in 100s from 1 View](https://mlsanity.com/paper/2312.15258/) (2023)
- [TriHuman : A Real-time and Controllable Tri-plane Representation for Detailed Human Geometry and Appearance Synthesis](https://mlsanity.com/paper/2312.05161/) (2023)
- [Recent Trends in 3D Reconstruction of General Non-Rigid Scenes](https://mlsanity.com/paper/2403.15064/) (2024)
- [Animatable and Relightable Gaussians for High-fidelity Human Avatar Modeling](https://mlsanity.com/paper/2311.16096/) (2023)
- [MANUS: Markerless Grasp Capture using Articulated 3D Gaussians](https://mlsanity.com/paper/2312.02137/) (2023)
- [GALA: Generating Animatable Layered Assets from a Single Scan](https://mlsanity.com/paper/2401.12979/) (2024)
- [Expressive Gaussian Human Avatars from Monocular RGB Video](https://mlsanity.com/paper/2407.03204/) (2024)
- [DIPO: Dual-State Images Controlled Articulated Object Generation Powered by Diverse Data](https://mlsanity.com/paper/2505.20460/) (2025)
- [BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesis](https://mlsanity.com/paper/2311.05521/) (2023)
- [IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing](https://mlsanity.com/paper/2312.05210/) (2023)
- [Guess The Unseen: Dynamic 3D Scene Reconstruction from Partial 2D Glimpses](https://mlsanity.com/paper/2404.14410/) (2024)
- [iHuman: Instant Animatable Digital Humans From Monocular Videos](https://mlsanity.com/paper/2407.11174/) (2024)
- [Survey on Modeling of Human-made Articulated Objects](https://mlsanity.com/paper/2403.14937/) (2024)
- [Surfel-based Gaussian Inverse Rendering for Fast and Relightable Dynamic Human Reconstruction from Monocular Video](https://mlsanity.com/paper/2407.15212/) (2024)
- [TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars for Augmented Reality via 3D Gaussian Splatting](https://mlsanity.com/paper/2503.17032/) (2025)
- [DEGAS: Detailed Expressions on Full-Body Gaussian Avatars](https://mlsanity.com/paper/2408.10588/) (2024)

## Abstract (arXiv)

Neural fields have revolutionized the area of 3D reconstruction and novel view synthesis of rigid scenes. A key challenge in making such methods applicable to articulated objects, such as the human body, is to model the deformation of 3D locations between the rest pose (a canonical space) and the deformed space. We propose a new articulation module for neural fields, Fast-SNARF, which finds accurate correspondences between canonical space and posed space via iterative root finding. Fast-SNARF is a drop-in replacement in functionality to our previous work, SNARF, while significantly improving its computational efficiency. We contribute several algorithmic and implementation improvements over SNARF, yielding a speed-up of 150×. These improvements include voxel-based correspondence search, pre-computing the linear blend skinning function, and an efficient software implementation with CUDA kernels. Fast-SNARF enables efficient and simultaneous optimization of shape and skinning weights given deformed observations without correspondences (e.g. 3D meshes). Because learning of deformation maps is a crucial component in many 3D human avatar methods and since Fast-SNARF provides a computationally efficient solution, we believe that this work represents a significant step towards the practical creation of 3D virtual humans.