Transformer models with person ID embeddings generate plausible reactive motions from paired boxing interaction data, with the simple Transformer outperforming iTransformer and Crossformer in stability and avoiding posture collapse.
Denoising diffusion probabilistic models
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Learning Reactive Human Motion Generation from Paired Interaction Data Using Transformer-Based Models
Transformer models with person ID embeddings generate plausible reactive motions from paired boxing interaction data, with the simple Transformer outperforming iTransformer and Crossformer in stability and avoiding posture collapse.