# MetaSapiens: Real-Time Neural Rendering with Efficiency-Aware Pruning and Accelerated Foveated Rendering

arXiv: https://arxiv.org/abs/2407.00435 · HTML: https://mlsanity.com/paper/2407.00435/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2024.

## Built on by (most cited first, 27 of 27)

- [LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering](https://mlsanity.com/paper/2505.23158/) (2025)
- [Mobile-GS: Real-time Gaussian Splatting for Mobile Devices](https://mlsanity.com/paper/2603.11531/) (2026)
- [GradiSeg: Gradient-Guided Gaussian Segmentation with Enhanced 3D Boundary Precision](https://mlsanity.com/paper/2412.00392/) (2024)
- [Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting](https://mlsanity.com/paper/2506.12400/) (2025)
- [SwinGS: Sliding Window Gaussian Splatting for Volumetric Video Streaming with Arbitrary Length](https://mlsanity.com/paper/2409.07759/) (2024)
- [FlexGaussian: Flexible and Cost-Effective Training-Free Compression for 3D Gaussian Splatting](https://mlsanity.com/paper/2507.06671/) (2025)
- [2D Triangle Splatting for Direct Differentiable Mesh Training](https://mlsanity.com/paper/2506.18575/) (2025)
- [GS-Scale: Unlocking Large-Scale 3D Gaussian Splatting Training via Host Offloading](https://mlsanity.com/paper/2509.15645/) (2025)
- [Neo: Real-Time On-Device 3D Gaussian Splatting with Reuse-and-Update Sorting Acceleration](https://mlsanity.com/paper/2511.12930/) (2025)
- [STREAMINGGS: Voxel-Based Streaming 3D Gaussian Splatting with Memory Optimization and Architectural Support](https://mlsanity.com/paper/2506.09070/) (2025)
- [CLM: Removing the GPU Memory Barrier for 3D Gaussian Splatting](https://mlsanity.com/paper/2511.04951/) (2025)
- [Foveated Reasoning: Stateful, Action-based Visual Focusing for Vision-Language Models](https://mlsanity.com/paper/2604.21079/) (2026)
- [Efficient 3D Gaussian Splatting with Axis-Shared Rasterization and Order-independent Transmittance](https://mlsanity.com/paper/2506.07069/) (2025)
- [PocketGS: On-Device Training of 3D Gaussian Splatting for High Perceptual Modeling](https://mlsanity.com/paper/2601.17354/) (2026)
- [VersaQ-3D: Architecture Support for Visual Geometry Grounded Transformers via Versatile Quantization](https://mlsanity.com/paper/2601.20317/) (2026)
- [GRTX: Efficient Ray Tracing for 3D Gaussian-Based Rendering](https://mlsanity.com/paper/2601.20429/) (2026)
- [Hybrid Foveated Path Tracing with Peripheral Gaussians for Immersive Anatomy](https://mlsanity.com/paper/2601.22026/) (2026)
- [Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication](https://mlsanity.com/paper/2606.18588/) (2026)
- [DeGS: A Scalable 3DGS Architecture via Decoupled Workload Parsing and Reorganization](https://mlsanity.com/paper/2608.02099/) (2026)
- [EyeNavGS: A 6-DoF Navigation Dataset and Record-n-Replay Software for Real-World 3DGS Scenes in VR](https://mlsanity.com/paper/2506.02380/) (2025)
- [A3FR: Agile 3D Gaussian Splatting with Incremental Gaze Tracked Foveated Rendering in Virtual Reality](https://mlsanity.com/paper/2507.04147/) (2025)
- [HybridSplat: Fast Reflection-baked Gaussian Tracing using Hybrid Splatting](https://mlsanity.com/paper/2512.08334/) (2025)
- [Seeing What Matters: Perceptual Wrapper with Common Randomness for 3D Gaussian Splatting](https://mlsanity.com/paper/2606.11782/) (2026)
- [From Splats to Silicon: Rethinking Computational Efficiency of 3DGS](https://mlsanity.com/paper/2609.06157/) (2026)
- [ControlGS: Conditioning Neural Gaussians for Downstream-Processing-Aware XR Rendering](https://mlsanity.com/paper/2609.32038/) (2026)
- [GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Prediction](https://mlsanity.com/paper/2609.38519/) (2026)
- [GLoD-LVSM: Learning Geometry-Grounded Continuous-LoD Scene Representations for Large View Synthesis Models](https://mlsanity.com/paper/2610.20254/) (2026)

## Abstract (arXiv)

Point-Based Neural Rendering (PBNR) is emerging as a promising class of rendering techniques, which are permeating all aspects of society, driven by a growing demand for real-time, photorealistic rendering in AR/VR and digital twins. Achieving real-time PBNR on mobile devices is challenging. This paper proposes MetaSapiens, a PBNR system that for the first time delivers real-time neural rendering on mobile devices while maintaining human visual quality. MetaSapiens combines three techniques. First, we present an efficiency-aware pruning technique to optimize rendering speed. Second, we introduce a Foveated Rendering (FR) method for PBNR, leveraging humans' low visual acuity in peripheral regions to relax rendering quality and improve rendering speed. Finally, we propose an accelerator design for FR, addressing the load imbalance issue in (FR-based) PBNR. Our evaluation shows that our system achieves an order of magnitude speedup over existing PBNR models without sacrificing subjective visual quality, as confirmed by a user study. The code and demo are available at: https://horizon-lab.org/metasapiens/.