# Deformable 3D Gaussian Splatting for Animatable Human Avatars

arXiv: https://arxiv.org/abs/2312.15059 · HTML: https://mlsanity.com/paper/2312.15059/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 81 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 30 of 52)

- [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)
- [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)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction](https://mlsanity.com/paper/2309.13101/) (2023)
- [Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis](https://mlsanity.com/paper/2308.09713/) (2023)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://mlsanity.com/paper/2012.15838/) (2020)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video](https://mlsanity.com/paper/2201.04127/) (2022)
- [Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies](https://mlsanity.com/paper/2105.02872/) (2021)
- [Towards Real-World Blind Face Restoration with Generative Facial Prior](https://mlsanity.com/paper/2101.04061/) (2021)
- [Dense Depth Priors for Neural Radiance Fields from Sparse Input Views](https://mlsanity.com/paper/2112.03288/) (2021)
- [NeuMan: Neural Human Radiance Field from a Single Video](https://mlsanity.com/paper/2203.12575/) (2022)
- [A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose](https://mlsanity.com/paper/2102.06199/) (2021)
- [SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes](https://mlsanity.com/paper/2104.03953/) (2021)
- [Geometric Transformer for Fast and Robust Point Cloud Registration](https://mlsanity.com/paper/2202.06688/) (2022)
- [Neural Articulated Radiance Field](https://mlsanity.com/paper/2104.03110/) (2021)
- [PPFNet: Global Context Aware Local Features for Robust 3D Point Matching](https://mlsanity.com/paper/1802.02669/) (2018)
- [SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks](https://mlsanity.com/paper/2104.03313/) (2021)
- [TAVA: Template-free Animatable Volumetric Actors](https://mlsanity.com/paper/2206.08929/) (2022)
- [Learning to Reconstruct People in Clothing from a Single RGB Camera](https://mlsanity.com/paper/1903.05885/) (2019)
- [DeepCap: Monocular Human Performance Capture Using Weak Supervision](https://mlsanity.com/paper/2003.08325/) (2020)

## Built on by (most cited first, 30 of 44)

- [A Survey on 3D Gaussian Splatting](https://mlsanity.com/paper/2401.03890/) (2024)
- [3D Gaussian as a New Era: A Survey](https://mlsanity.com/paper/2402.07181/) (2024)
- [3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting](https://mlsanity.com/paper/2404.00409/) (2024)
- [ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting](https://mlsanity.com/paper/2502.19459/) (2025)
- [Representing Long Volumetric Video with Temporal Gaussian Hierarchy](https://mlsanity.com/paper/2412.09608/) (2024)
- [DifFRelight: Diffusion-Based Facial Performance Relighting](https://mlsanity.com/paper/2410.08188/) (2024)
- [Spectrally Pruned Gaussian Fields with Neural Compensation](https://mlsanity.com/paper/2405.00676/) (2024)
- [Vid2Avatar-Pro: Authentic Avatar from Videos in the Wild via Universal Prior](https://mlsanity.com/paper/2503.01610/) (2025)
- [GVA: Reconstructing Vivid 3D Gaussian Avatars from Monocular Videos](https://mlsanity.com/paper/2402.16607/) (2024)
- [Dynamic Gaussians Mesh: Consistent Mesh Reconstruction from Dynamic Scenes](https://mlsanity.com/paper/2404.12379/) (2024)
- [Expressive Gaussian Human Avatars from Monocular RGB Video](https://mlsanity.com/paper/2407.03204/) (2024)
- [MTGS: Multi-Traversal Gaussian Splatting](https://mlsanity.com/paper/2503.12552/) (2025)
- [VideoArtGS: Building Digital Twins of Articulated Objects from Monocular Video](https://mlsanity.com/paper/2509.17647/) (2025)
- [ToMiE: Towards Explicit Exoskeleton for the Reconstruction of Complicated 3D Human Avatars](https://mlsanity.com/paper/2410.08082/) (2024)
- [MaGS: Reconstructing and Simulating Dynamic 3D Objects with Mesh-adsorbed Gaussian Splatting](https://mlsanity.com/paper/2406.01593/) (2024)
- [Sequential Gaussian Avatars with Hierarchical Motion Context](https://mlsanity.com/paper/2411.16768/) (2024)
- [Gaussians-to-Life: Text-Driven Animation of 3D Gaussian Splatting Scenes](https://mlsanity.com/paper/2411.19233/) (2024)
- [OccFusion: Rendering Occluded Humans with Generative Diffusion Priors](https://mlsanity.com/paper/2407.00316/) (2024)
- [MPMAvatar: Learning 3D Gaussian Avatars with Accurate and Robust Physics-Based Dynamics](https://mlsanity.com/paper/2510.01619/) (2025)
- [PromptVFX: Text-Driven Fields for Open-World 3D Gaussian Animation](https://mlsanity.com/paper/2506.01091/) (2025)
- [ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction](https://mlsanity.com/paper/2509.03775/) (2025)
- [Generalizable Neural Human Renderer](https://mlsanity.com/paper/2404.14199/) (2024)
- [R3-Avatar: Record and Retrieve Temporal Codebook for Reconstructing Photorealistic Human Avatars](https://mlsanity.com/paper/2503.12751/) (2025)
- [ImViD: Immersive Volumetric Videos for Enhanced VR Engagement](https://mlsanity.com/paper/2503.14359/) (2025)
- [Parametric Gaussian Human Model: Generalizable Prior for Efficient and Realistic Human Avatar Modeling](https://mlsanity.com/paper/2506.06645/) (2025)
- [FreeCloth: Free-form Generation Enhances Challenging Clothed Human Modeling](https://mlsanity.com/paper/2411.19942/) (2024)
- [Splatography: Sparse multi-view dynamic Gaussian Splatting for filmmaking challenges](https://mlsanity.com/paper/2511.05152/) (2025)
- [ExFMan: Rendering 3D Dynamic Humans with Hybrid Monocular Blurry Frames and Events](https://mlsanity.com/paper/2409.14103/) (2024)
- [StyledStreets: Multi-style Street Simulator with Spatial and Temporal Consistency](https://mlsanity.com/paper/2503.21104/) (2025)
- [Part2GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting](https://mlsanity.com/paper/2506.17212/) (2025)

## Abstract (arXiv)

Recent advances in neural radiance fields enable novel view synthesis of photo-realistic images in dynamic settings, which can be applied to scenarios with human animation. Commonly used implicit backbones to establish accurate models, however, require many input views and additional annotations such as human masks, UV maps and depth maps. In this work, we propose ParDy-Human (Parameterized Dynamic Human Avatar), a fully explicit approach to construct a digital avatar from as little as a single monocular sequence. ParDy-Human introduces parameter-driven dynamics into 3D Gaussian Splatting where 3D Gaussians are deformed by a human pose model to animate the avatar. Our method is composed of two parts: A first module that deforms canonical 3D Gaussians according to SMPL vertices and a consecutive module that further takes their designed joint encodings and predicts per Gaussian deformations to deal with dynamics beyond SMPL vertex deformations. Images are then synthesized by a rasterizer. ParDy-Human constitutes an explicit model for realistic dynamic human avatars which requires significantly fewer training views and images. Our avatars learning is free of additional annotations such as masks and can be trained with variable backgrounds while inferring full-resolution images efficiently even on consumer hardware. We provide experimental evidence to show that ParDy-Human outperforms state-of-the-art methods on ZJU-MoCap and THUman4.0 datasets both quantitatively and visually.