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UDE: A Unified Driving Engine for Human Motion Generation

CVPR 2023 · Video › Video generation · approach: Text-to-motion diffusion · 55 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace GRU-based UTT with GPT-based UTT (ablation)UDE · HumanML3DTop-1 Acc.: 7.89 -> 8.11Helped
Replace GRU-based UTT with GPT-based UTT (ablation)UDE · AIST++FID_K: 47.21 -> 28.44Helped
Inject Gaussian noise z into token prediction (ablation)UDE UTT · HumanML3DDiv_k: 4.28 -> 5.05Helped
Inject Gaussian noise z into token prediction (ablation)UDE UTT · HumanML3DTop-1 Acc.: 8.11 -> 8.33Helped
Inject Gaussian noise z into token prediction (ablation)UDE UTT · AIST++FID_k: 28.44 -> 27.99Helped
Replace VQ decoder with diffusion motion decoder (ablation)UDE · AIST++FID_k: 28.44 to 18.43Helped
Replace VQ decoder with diffusion motion decoder (ablation)UDE · HumanML3DDiv_k: 4.28 -> 6.98Helped
Use unified text-and-audio training instead of text-only training (ablation)UDE · HumanML3DTop-1 Acc.: 7.77 -> 8.11Helped
Use unified text-and-audio training instead of text-only training (ablation)UDE · HumanML3DFID_k: 31.73 -> 27.66Helped
Use unified text-and-audio training instead of audio-only training (ablation)UDE · AIST++Beat Align: 0.2231 -> 0.2268Helped
Train one unified model rather than modality-specific models (ablation)UDE · AIST++FID_m: 11.65 (audio only); 15.70 (unified)Mixed

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