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Generating Diverse and Natural 3D Human Motions From Text

CVPR 2022 · 3D vision › 3D generation · approach: Scene-aware human motion synthesis · 553 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use text2length sampling rather than real motion lengthsText2motion generation · HumanML3DMultiModality: 2.090±.083 -> 2.219±.074Helped
Use text2length sampling rather than real motion lengthsText2motion generation · HumanML3DFID: 1.067±.002 -> 1.087±.021Hurt
Use text2length sampling rather than real motion lengthsText2motion generation · KIT-MLMultiModality: 1.482±.065 -> 2.052±.107Helped
Use text2length sampling rather than real motion lengthsText2motion generation · KIT-MLFID: 2.770±.109 -> 3.022±.107Hurt
Add motion snippet codes (ablation)Text2motion generation · HumanML3DR Precision Top 1: 0.370±.002 -> 0.455±.003Helped
Add motion snippet codes (ablation)Text2motion generation · HumanML3DFID: 1.200±.027 -> 1.087±.021Helped
Add local word attention (ablation)Text2motion generation · HumanML3DR Precision Top 1: 0.396±.002 -> 0.455±.003Helped
Add local word attention (ablation)Text2motion generation · HumanML3DFID: 1.833±.032 -> 1.087±.021Helped
Add part-of-speech tags (ablation)Text2motion generation · HumanML3DR Precision Top 1: 0.443±.003 -> 0.455±.003Helped
Add part-of-speech tags (ablation)Text2motion generation · HumanML3DFID: 1.157±.016 -> 1.087±.021Helped
Add time-to-arrival positional encoding (ablation)Text2motion generation · HumanML3DR Precision Top 1: 0.444±.005 -> 0.455±.003Helped
Add time-to-arrival positional encoding (ablation)Text2motion generation · HumanML3DFID: 1.229±.020 -> 1.087±.021Helped

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