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3 months ago

Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars

Egor Zakharov Aleksei Ivakhnenko Aliaksandra Shysheya Victor Lempitsky

Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars

Abstract

We propose a neural rendering-based system that creates head avatars from a single photograph. Our approach models a person's appearance by decomposing it into two layers. The first layer is a pose-dependent coarse image that is synthesized by a small neural network. The second layer is defined by a pose-independent texture image that contains high-frequency details. The texture image is generated offline, warped and added to the coarse image to ensure a high effective resolution of synthesized head views. We compare our system to analogous state-of-the-art systems in terms of visual quality and speed. The experiments show significant inference speedup over previous neural head avatar models for a given visual quality. We also report on a real-time smartphone-based implementation of our system.

Code Repositories

saic-violet/bilayer-model
Official
pytorch
Mentioned in GitHub

Benchmarks

BenchmarkMethodologyMetrics
talking-head-generation-on-voxceleb2-1-shotFast Bi-layer Avatars (medium size)
CSIM: 0.653
LPIPS: 0.358
Normalized Pose Error: 43.3
SSIM: 0.508
inference time (ms): 4
talking-head-generation-on-voxceleb2-1-shotFew-shot Vid-to-vid (medium size)
CSIM: 0.604
LPIPS: 0.368
Normalized Pose Error: 46.1
SSIM: 0.419
inference time (ms): 22
talking-head-generation-on-voxceleb2-1-shotFirst Order Motion Model (medium size)
CSIM: 0.638
LPIPS: 0.311
Normalized Pose Error: 47.8
SSIM: 0.553
inference time (ms): 13

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Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars | Papers | HyperAI