# High-Fidelity Human Avatars from Laptop Webcams using Edge Compute

arXiv: https://arxiv.org/abs/2502.02468 · HTML: https://mlsanity.com/paper/2502.02468/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 4 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [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)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction](https://mlsanity.com/paper/2012.03065/) (2020)
- [3D Morphable Face Models -- Past, Present and Future](https://mlsanity.com/paper/1909.01815/) (2019)
- [Reconstructing Personalized Semantic Facial NeRF Models From Monocular Video](https://mlsanity.com/paper/2210.06108/) (2022)
- [AvatarMAV: Fast 3D Head Avatar Reconstruction Using Motion-Aware Neural Voxels](https://mlsanity.com/paper/2211.13206/) (2022)
- [High-Fidelity 3D Digital Human Head Creation from RGB-D Selfies](https://mlsanity.com/paper/2010.05562/) (2020)
- [FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction](https://mlsanity.com/paper/2211.13874/) (2022)

## Abstract (arXiv)

Photo-realistic human avatars have broad applications, yet high-fidelity avatar generation has traditionally required expensive professional camera rigs and extensive artistic labor. Recent research has enabled constructing them automatically from smartphones with RGB and IR sensors, however, these new methods still rely on high-resolution cameras on modern smartphones and often require offloading the processing to powerful servers with GPUs. Modern applications such as video conferencing call for the ability to generate these avatars from consumer-grade laptop webcams using limited compute available on-device. In this work, we develop a novel method based on 3D morphable models, landmark detection, photorealistic texture GANs, and differentiable rendering to tackle the problem of low webcam image quality and edge computation. We build an automatic system to generate high-fidelity animatable avatars under these limitations, leveraging the compute capabilities of AMD mobile processors.