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ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model

NeurIPS 2022 · 3D vision › Face and shape · approach: 3D shape generative modeling · 26 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
recursive generation with phrase sequences instead of single-step AutoSDFShapeCrafter · Text2Shape chairsCLIP-S: 48.92 -> 52.43Helped
recursive generation instead of AutoSDFShapeCrafter · Text2Shape chairsFID: 18.45 -> 16.36Helped
recursive generation vs AutoSDF as phrase count growsShapeCrafter · Text2Shape++ >4 phrasesFID: 22.47 -> 16.89Helped
recursive generation vs AutoSDF on short inputsShapeCrafter · Text2Shape++ 1-2 phrasesSGLOT-C: 0.53 -> 0.47Hurt
add conditional training with residual blocks (ablation)ShapeCrafter · Text2Shape chairsCLIP-S: 49.53 -> 52.43Helped
add conditional training with residual blocks (ablation)ShapeCrafter · Text2Shape chairsFID: 20.22 -> 16.36Helped
add random transformer after residual blocks (ablation)ShapeCrafter · Text2Shape chairsFID: 19.83 -> 16.36Helped
add random transformer after residual blocks (ablation)ShapeCrafter · Text2Shape chairsCLIP-S: 49.29 -> 52.43Helped
reorder transformer generation by distribution change (ablation)ShapeCrafter · Text2Shape chairsCLIP-S: 42.47 -> 52.43Helped
reorder transformer generation by distribution change (ablation)ShapeCrafter · Text2Shape chairsFID: 16.67 -> 16.36Helped
add more phrases to the descriptionShapeCrafter · latent distributionmean entropy H: 0.423 -> 0.267Helped

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