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3D-Aware Conditional Image Synthesis

CVPR 2023 · 3D vision › NeRF and radiance fields · approach: 3D-aware generative radiance fields · 29 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add a 3D label-rendering branch (ablation)pix2pix3D · Seg2FacemIoU: N/A (0.43) -> 0.51 (0.52)Helped
add a 3D label-rendering branch (ablation)pix2pix3D · Edge2CarAP: 0.45 (0.42) -> 0.63 (0.59)Helped
add cross-view consistency loss (ablation)pix2pix3D · Seg2FaceFVV Identity: 0.42 -> 0.36Helped
add cross-view consistency loss (ablation)pix2pix3D · Edge2CarFID: 9.42 -> 8.31Helped
add the image discriminator (ablation)pix2pix3D · Seg2FaceFID: 15.32 -> 11.54Helped
add the conditional discriminator (ablation)pix2pix3D · Seg2FacemIoU: 0.37 (0.47) -> 0.51 (0.52)Helped
concatenate RGB images into the conditional discriminator input (ablation)pix2pix3D · Seg2FacemIoU: 0.41 (0.40) -> 0.51 (0.52)Helped
use pretrained EG3D weights to initialize parts of the model (ablation)pix2pix3D · Seg2FaceFID: 11.54 -> 11.13Helped
sample random poses with probability 0 instead of 0.5 (ablation)pix2pix3D · training pose samplingimage qualityHurt
sample random poses with probability 1 instead of 0.5 (ablation)pix2pix3D · training pose samplinginput alignmentHurt
remove the image discriminator (ablation)pix2pix3D · Seg2FaceFID: 11.54 -> 15.32Hurt
remove the conditional discriminator (ablation)pix2pix3D · Seg2FacemIoU: 0.51 (0.52) -> 0.37 (0.47)Hurt

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