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Research map / 3D vision / Face and shape

Textured Neural Avatars

CVPR 2019 · 3D vision › Face and shape · approach: Gaussian and implicit head avatars · 112 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Represent appearance with explicit learned textures and predict texture coordinates instead of directly translating pose to RGBTextured Neural Avatars · CMU Panoptic hold-out cameras and motionsSSIM: 0.908 -> 0.919; 0.916 -> 0.922; 0.905 -> 0.914; 0.918 -> 0.920Helped
Represent appearance with explicit learned textures and predict texture coordinates instead of directly translating pose to RGBTextured Neural Avatars · CMU Panoptic hold-out cameras and motionsFrechet distance: 6.7 -> 8.8; 7.0 -> 10.7; 7.7 -> 8.9; 7.0 -> 10.4Hurt
Replace direct RGB-and-mask prediction with textured body-part assignments and coordinates (ablation)Direct baseline · CMU Panoptic hold-out cameras and motionsSSIM: 0.899 -> 0.919; 0.907 -> 0.922; 0.896 -> 0.914; 0.907 -> 0.920Helped
Replace direct RGB-and-mask prediction with textured body-part assignments and coordinates (ablation)Direct baseline · CMU Panoptic hold-out cameras and motionsFrechet distance: 7.3 -> 8.8; 8.8 -> 10.7; 10.7 -> 8.9; 9.7 -> 10.4Mixed
Add DensePose outputs as input alongside keypoints (ablation)V2V on CMU1-6 · CMU1-6considered metricsNo clear effect
Use the textured avatar rather than V2V when fine-tuning from a single videoSingle-video avatar · Hold-out camera and motion, two subjectsuser preference rate: 55%; 65%Helped

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