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Structured Local Radiance Fields for Human Avatar Modeling

CVPR 2022 · 3D vision › NeRF and radiance fields · approach: Single-view and streaming radiance fields · 125 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use structured local radiance fields with node residual translations and dynamic detail embeddings instead of Animatable NeRFClothed human avatar modeling · Hoody, Jacket, Dress1, and Dress2 novel-pose synthesisPSNR: 22.43 -> 24.94; 24.30 -> 25.24; 19.52 -> 23.43; 20.49 -> 22.19Helped
Use structured local radiance fields with node residual translations and dynamic detail embeddings instead of Animatable NeRFClothed human avatar modeling · Hoody, Jacket, Dress1, and Dress2 novel-pose synthesisSSIM: 0.893 -> 0.928; 0.909 -> 0.927; 0.848 -> 0.891; 0.877 -> 0.900Helped
Use structured local radiance fields with pose-conditioned node variables instead of Neural BodyClothed human avatar modeling · ZJU-MoCap 387 and 392, seen and unseen posesPSNR: 25.79 -> 28.32; 21.60 -> 23.61; 29.44 -> 30.79; 25.76 -> 26.74Helped
Use structured local radiance fields with pose-conditioned node variables instead of Neural BodyClothed human avatar modeling · ZJU-MoCap 387 and 392, seen and unseen posesSSIM: 0.928 -> 0.953; 0.870 -> 0.905; 0.946 -> 0.958; 0.909 -> 0.927Helped
Add node residual translations and dynamic detail embeddings (ablation)Structured local radiance fields · Dress sequence, training posesPSNR: 17.52 -> 21.48Helped
Add dynamic detail embeddings (ablation)Structured local radiance fields with node residual translations · Dress sequence, training posesPSNR: 20.47 -> 21.48Helped
Replace cVAE with deterministic regression from pose (ablation)Node-related variable prediction · Training framesPSNR: 25.62 -> 25.34Hurt
Replace the time-stamp input with learnable per-frame latent codes (ablation)cVAE node-related variable prediction · Node motion trajectoriestemporal smoothnessHurt

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