# R2Human: Real-Time 3D Human Appearance Rendering from a Single Image

arXiv: https://arxiv.org/abs/2312.05826 · HTML: https://mlsanity.com/paper/2312.05826/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 10 Dec 2023.

## Builds on (most cited first, 21 of 21)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [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)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [ICON: Implicit Clothed humans Obtained from Normals](https://mlsanity.com/paper/2112.09127/) (2021)
- [Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors](https://mlsanity.com/paper/2105.01859/) (2021)
- [PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction](https://mlsanity.com/paper/2007.03858/) (2020)
- [DeepHuman: 3D Human Reconstruction from a Single Image](https://mlsanity.com/paper/1903.06473/) (2019)
- [BodyNet: Volumetric Inference of 3D Human Body Shapes](https://mlsanity.com/paper/1804.04875/) (2018)
- [Robust High-Resolution Video Matting with Temporal Guidance](https://mlsanity.com/paper/2108.11515/) (2021)
- [Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing](https://mlsanity.com/paper/2204.08906/) (2022)
- [SHERF: Generalizable Human NeRF from a Single Image](https://mlsanity.com/paper/2303.12791/) (2023)
- [High-fidelity 3D Human Digitization from Single 2K Resolution Images](https://mlsanity.com/paper/2303.15108/) (2023)
- [DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Rendering](https://mlsanity.com/paper/2106.03798/) (2021)
- [FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction](https://mlsanity.com/paper/2206.02194/) (2022)
- [D-IF: Uncertainty-aware Human Digitization via Implicit Distribution Field](https://mlsanity.com/paper/2308.08857/) (2023)
- [Free-Viewpoint RGB-D Human Performance Capture and Rendering](https://mlsanity.com/paper/2112.13889/) (2021)
- [HDhuman: High-quality Human Novel-view Rendering from Sparse Views](https://mlsanity.com/paper/2201.08158/) (2022)

## Built on by (most cited first, 5 of 5)

- [PixelGaussian: Generalizable 3D Gaussian Reconstruction from Arbitrary Views](https://mlsanity.com/paper/2410.18979/) (2024)
- [GST: Precise 3D Human Body from a Single Image with Gaussian Splatting Transformers](https://mlsanity.com/paper/2409.04196/) (2024)
- [MultiGO++: Monocular 3D Clothed Human Reconstruction via Geometry-Texture Collaboration](https://mlsanity.com/paper/2603.04993/) (2026)
- [FOF-X: Towards Real-time Detailed Human Reconstruction from a Single Image](https://mlsanity.com/paper/2412.05961/) (2024)
- [OAHuman: Occlusion-Aware 3D Human Reconstruction from Monocular Images](https://mlsanity.com/paper/2603.14249/) (2026)

## Abstract (arXiv)

Rendering 3D human appearance from a single image in real-time is crucial for achieving holographic communication and immersive VR/AR. Existing methods either rely on multi-camera setups or are constrained to offline operations. In this paper, we propose R2Human, the first approach for real-time inference and rendering of photorealistic 3D human appearance from a single image. The core of our approach is to combine the strengths of implicit texture fields and explicit neural rendering with our novel representation, namely Z-map. Based on this, we present an end-to-end network that performs high-fidelity color reconstruction of visible areas and provides reliable color inference for occluded regions. To further enhance the 3D perception ability of our network, we leverage the Fourier occupancy field as a prior for generating the texture field and providing a sampling surface in the rendering stage. We also propose a consistency loss and a spatial fusion strategy to ensure the multi-view coherence. Experimental results show that our method outperforms the state-of-the-art methods on both synthetic data and challenging real-world images, in real-time. The project page can be found at http://cic.tju.edu.cn/faculty/likun/projects/R2Human.