# Geometry-Guided Progressive NeRF for Generalizable and Efficient Neural Human Rendering

arXiv: https://arxiv.org/abs/2112.04312 · HTML: https://mlsanity.com/paper/2112.04312/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 48 references that are in the map.
- First posted on arXiv 8 Dec 2021.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [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)
- [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision](https://mlsanity.com/paper/1912.07372/) (2019)
- [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://mlsanity.com/paper/2012.15838/) (2020)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [DeepVoxels: Learning Persistent 3D Feature Embeddings](https://mlsanity.com/paper/1812.01024/) (2018)
- [Neural Point-Based Graphics](https://mlsanity.com/paper/1906.08240/) (2019)
- [DeepView: View Synthesis with Learned Gradient Descent](https://mlsanity.com/paper/1906.07316/) (2019)
- [DeepHuman: 3D Human Reconstruction from a Single Image](https://mlsanity.com/paper/1903.06473/) (2019)
- [DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracing](https://mlsanity.com/paper/1911.13225/) (2019)
- [Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering](https://mlsanity.com/paper/2109.07448/) (2021)
- [Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control](https://mlsanity.com/paper/2106.02019/) (2021)
- [Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis](https://mlsanity.com/paper/1912.05237/) (2019)
- [STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering](https://mlsanity.com/paper/2101.01602/) (2020)
- [Monocular Real-Time Volumetric Performance Capture](https://mlsanity.com/paper/2007.13988/) (2020)
- [SiCloPe: Silhouette-Based Clothed People](https://mlsanity.com/paper/1901.00049/) (2018)
- [3D Human Texture Estimation from a Single Image with Transformers](https://mlsanity.com/paper/2109.02563/) (2021)
- [Transformable Bottleneck Networks](https://mlsanity.com/paper/1904.06458/) (2019)
- [Data-Driven 3D Reconstruction of Dressed Humans From Sparse Views](https://mlsanity.com/paper/2104.08013/) (2021)

## Built on by (most cited first, 30 of 46)

- [AvatarGen: A 3D Generative Model for Animatable Human Avatars](https://mlsanity.com/paper/2211.14589/) (2022)
- [MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human Captures](https://mlsanity.com/paper/2312.02963/) (2023)
- [Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis](https://mlsanity.com/paper/2204.11798/) (2022)
- [Generalizable Human Gaussians for Sparse View Synthesis](https://mlsanity.com/paper/2407.12777/) (2024)
- [Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos](https://mlsanity.com/paper/2203.08133/) (2022)
- [HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video](https://mlsanity.com/paper/2304.12281/) (2023)
- [Vid2Avatar-Pro: Authentic Avatar from Videos in the Wild via Universal Prior](https://mlsanity.com/paper/2503.01610/) (2025)
- [Generalizable Human Gaussians from Single-View Image](https://mlsanity.com/paper/2406.06050/) (2024)
- [Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors](https://mlsanity.com/paper/2208.11905/) (2022)
- [PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision](https://mlsanity.com/paper/2203.15625/) (2022)
- [GM-NeRF: Learning Generalizable Model-based Neural Radiance Fields from Multi-view Images](https://mlsanity.com/paper/2303.13777/) (2023)
- [Drivable Avatar Clothing: Faithful Full-Body Telepresence with Dynamic Clothing Driven by Sparse RGB-D Input](https://mlsanity.com/paper/2310.05917/) (2023)
- [Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling](https://mlsanity.com/paper/2304.04897/) (2023)
- [TransHuman: A Transformer-based Human Representation for Generalizable Neural Human Rendering](https://mlsanity.com/paper/2307.12291/) (2023)
- [XAGen: 3D Expressive Human Avatars Generation](https://mlsanity.com/paper/2311.13574/) (2023)
- [Rendering Humans from Object-Occluded Monocular Videos](https://mlsanity.com/paper/2308.04622/) (2023)
- [HumanNeRF-SE: A Simple yet Effective Approach to Animate HumanNeRF with Diverse Poses](https://mlsanity.com/paper/2312.02232/) (2023)
- [RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images](https://mlsanity.com/paper/2503.14198/) (2025)
- [NeRFPrior: Learning Neural Radiance Field as a Prior for Indoor Scene Reconstruction](https://mlsanity.com/paper/2503.18361/) (2025)
- [GHuNeRF: Generalizable Human NeRF from a Monocular Video](https://mlsanity.com/paper/2308.16576/) (2023)
- [SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation](https://mlsanity.com/paper/2404.15276/) (2024)
- [MVHumanNet++: A Large-scale Dataset of Multi-view Daily Dressing Human Captures with Richer Annotations for 3D Human Digitization](https://mlsanity.com/paper/2505.01838/) (2025)
- [Generalizable Neural Voxels for Fast Human Radiance Fields](https://mlsanity.com/paper/2303.15387/) (2023)
- [Neural Radiance Fields: Past, Present, and Future](https://mlsanity.com/paper/2304.10050/) (2023)
- [OccFusion: Rendering Occluded Humans with Generative Diffusion Priors](https://mlsanity.com/paper/2407.00316/) (2024)
- [SafeguardGS: 3D Gaussian Primitive Pruning While Avoiding Catastrophic Scene Destruction](https://mlsanity.com/paper/2405.17793/) (2024)
- [3D-Consistent Human Avatars with Sparse Inputs via Gaussian Splatting and Contrastive Learning](https://mlsanity.com/paper/2408.09663/) (2024)
- [ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency](https://mlsanity.com/paper/2412.01402/) (2024)
- [MF-NeRF: Memory Efficient NeRF with Mixed-Feature Hash Table](https://mlsanity.com/paper/2304.12587/) (2023)
- [Text-guided 3D Human Generation from 2D Collections](https://mlsanity.com/paper/2305.14312/) (2023)

## Abstract (arXiv)

In this work we develop a generalizable and efficient Neural Radiance Field (NeRF) pipeline for high-fidelity free-viewpoint human body synthesis under settings with sparse camera views. Though existing NeRF-based methods can synthesize rather realistic details for human body, they tend to produce poor results when the input has self-occlusion, especially for unseen humans under sparse views. Moreover, these methods often require a large number of sampling points for rendering, which leads to low efficiency and limits their real-world applicability. To address these challenges, we propose a Geometry-guided Progressive NeRF (GP-NeRF). In particular, to better tackle self-occlusion, we devise a geometry-guided multi-view feature integration approach that utilizes the estimated geometry prior to integrate the incomplete information from input views and construct a complete geometry volume for the target human body. Meanwhile, for achieving higher rendering efficiency, we introduce a progressive rendering pipeline through geometry guidance, which leverages the geometric feature volume and the predicted density values to progressively reduce the number of sampling points and speed up the rendering process. Experiments on the ZJU-MoCap and THUman datasets show that our method outperforms the state-of-the-arts significantly across multiple generalization settings, while the time cost is reduced > 70% via applying our efficient progressive rendering pipeline.