# MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos

arXiv: https://arxiv.org/abs/2407.08414 · HTML: https://mlsanity.com/paper/2407.08414/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 75 of its 106 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 30 of 75)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis](https://mlsanity.com/paper/2012.03927/) (2020)
- [HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video](https://mlsanity.com/paper/2201.04127/) (2022)
- [Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis](https://mlsanity.com/paper/2111.04276/) (2021)
- [NeRD: Neural Reflectance Decomposition from Image Collections](https://mlsanity.com/paper/2012.03918/) (2020)
- [Extracting Triangular 3D Models, Materials, and Lighting From Images](https://mlsanity.com/paper/2111.12503/) (2021)
- [Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies](https://mlsanity.com/paper/2105.02872/) (2021)
- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting](https://mlsanity.com/paper/2104.00674/) (2021)
- [Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors](https://mlsanity.com/paper/2105.01859/) (2021)
- [NeuMan: Neural Human Radiance Field from a Single Video](https://mlsanity.com/paper/2203.12575/) (2022)
- [Video Based Reconstruction of 3D People Models](https://mlsanity.com/paper/1803.04758/) (2018)
- [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)
- [Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition](https://mlsanity.com/paper/2110.14373/) (2021)
- [PointAvatar: Deformable Point-based Head Avatars from Videos](https://mlsanity.com/paper/2212.08377/) (2022)
- [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)
- [TensoIR: Tensorial Inverse Rendering](https://mlsanity.com/paper/2304.12461/) (2023)
- [GauHuman: Articulated Gaussian Splatting from Monocular Human Videos](https://mlsanity.com/paper/2312.02973/) (2023)
- [Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising](https://mlsanity.com/paper/2206.03380/) (2022)
- [HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion](https://mlsanity.com/paper/2305.06356/) (2023)

## Built on by (most cited first, 30 of 34)

- [LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds](https://mlsanity.com/paper/2503.10625/) (2025)
- [Motion Anything: Any to Motion Generation](https://mlsanity.com/paper/2503.06955/) (2025)
- [AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction](https://mlsanity.com/paper/2412.02684/) (2024)
- [Vid2Avatar-Pro: Authentic Avatar from Videos in the Wild via Universal Prior](https://mlsanity.com/paper/2503.01610/) (2025)
- [Relightable Full-Body Gaussian Codec Avatars](https://mlsanity.com/paper/2501.14726/) (2025)
- [RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images](https://mlsanity.com/paper/2503.14198/) (2025)
- [Real-time High-fidelity Gaussian Human Avatars with Position-based Interpolation of Spatially Distributed MLPs](https://mlsanity.com/paper/2504.12909/) (2025)
- [SqueezeMe: Mobile-Ready Distillation of Gaussian Full-Body Avatars](https://mlsanity.com/paper/2412.15171/) (2024)
- [TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars for Augmented Reality via 3D Gaussian Splatting](https://mlsanity.com/paper/2503.17032/) (2025)
- [STG-Avatar: Animatable Human Avatars via Spacetime Gaussian](https://mlsanity.com/paper/2510.22140/) (2025)
- [RMAvatar: Photorealistic Human Avatar Reconstruction from Monocular Video Based on Rectified Mesh-embedded Gaussians](https://mlsanity.com/paper/2501.07104/) (2025)
- [HumanOLAT: A Large-Scale Dataset for Full-Body Human Relighting and Novel-View Synthesis](https://mlsanity.com/paper/2508.09137/) (2025)
- [Interactive Rendering of Relightable and Animatable Gaussian Avatars](https://mlsanity.com/paper/2407.10707/) (2024)
- [BEAM: Bridging Physically-based Rendering and Gaussian Modeling for Relightable Volumetric Video](https://mlsanity.com/paper/2502.08297/) (2025)
- [FRESA: Feedforward Reconstruction of Personalized Skinned Avatars from Few Images](https://mlsanity.com/paper/2503.19207/) (2025)
- [Generalizable and Relightable Gaussian Splatting for Human Novel View Synthesis](https://mlsanity.com/paper/2505.21502/) (2025)
- [MV-Performer: Taming Video Diffusion Model for Faithful and Synchronized Multi-view Performer Synthesis](https://mlsanity.com/paper/2510.07190/) (2025)
- [UMA: Ultra-detailed Human Avatars via Multi-level Surface Alignment](https://mlsanity.com/paper/2506.01802/) (2025)
- [Parametric Gaussian Human Model: Generalizable Prior for Efficient and Realistic Human Avatar Modeling](https://mlsanity.com/paper/2506.06645/) (2025)
- [Large-scale Codec Avatars: The Unreasonable Effectiveness of Large-scale Avatar Pretraining](https://mlsanity.com/paper/2604.02320/) (2026)
- [DNF-Avatar: Distilling Neural Fields for Real-time Animatable Avatar Relighting](https://mlsanity.com/paper/2504.10486/) (2025)
- [SVAD: From Single Image to 3D Avatar via Synthetic Data Generation with Video Diffusion and Data Augmentation](https://mlsanity.com/paper/2505.05475/) (2025)
- [Relightable Holoported Characters: Capturing and Relighting Dynamic Human Performance from Sparse Views](https://mlsanity.com/paper/2512.00255/) (2025)
- [High-Fidelity Mobile Avatars with Pruned Local Blendshapes](https://mlsanity.com/paper/2605.01854/) (2026)
- [Frequency-Decomposed Avatar Representation for Varying Camera Distances](https://mlsanity.com/paper/2512.03593/) (2025)
- [Mon3tr: Monocular 3D Telepresence with Pre-built Gaussian Avatars as Amortization](https://mlsanity.com/paper/2601.07518/) (2026)
- [DevilSight: Augmenting Monocular Human Avatar Reconstruction through a Virtual Perspective](https://mlsanity.com/paper/2509.00403/) (2025)
- [Autoregressive Appearance Prediction for 3D Gaussian Avatars](https://mlsanity.com/paper/2604.00928/) (2026)
- [MUA: Mobile Ultra-detailed Animatable Avatars](https://mlsanity.com/paper/2604.18583/) (2026)
- [Generalizable Human Gaussian Splatting via Multi-view Semantic Consistency](https://mlsanity.com/paper/2604.25466/) (2026)

## Abstract (arXiv)

We present a novel pipeline for learning high-quality triangular human avatars from multi-view videos. Recent methods for avatar learning are typically based on neural radiance fields (NeRF), which is not compatible with traditional graphics pipeline and poses great challenges for operations like editing or synthesizing under different environments. To overcome these limitations, our method represents the avatar with an explicit triangular mesh extracted from an implicit SDF field, complemented by an implicit material field conditioned on given poses. Leveraging this triangular avatar representation, we incorporate physics-based rendering to accurately decompose geometry and texture. To enhance both the geometric and appearance details, we further employ a 2D UNet as the network backbone and introduce pseudo normal ground-truth as additional supervision. Experiments show that our method can learn triangular avatars with high-quality geometry reconstruction and plausible material decomposition, inherently supporting editing, manipulation or relighting operations.