  Predicting human motion plays a crucial role in ensuring a safe and effective
human-robot close collaboration in intelligent remanufacturing systems of the
future. Existing works can be categorized into two groups: those focusing on
accuracy, predicting a single future motion, and those generating diverse
predictions based on observations. The former group fails to address the
uncertainty and multi-modal nature of human motion, while the latter group
often produces motion sequences that deviate too far from the ground truth or
become unrealistic within historical contexts. To tackle these issues, we
propose TransFusion, an innovative and practical diffusion-based model for 3D
human motion prediction which can generate samples that are more likely to
happen while maintaining a certain level of diversity. Our model leverages
Transformer as the backbone with long skip connections between shallow and deep
layers. Additionally, we employ the discrete cosine transform to model motion
sequences in the frequency space, thereby improving performance. In contrast to
prior diffusion-based models that utilize extra modules like cross-attention
and adaptive layer normalization to condition the prediction on past observed
motion, we treat all inputs, including conditions, as tokens to create a more
lightweight model compared to existing approaches. Extensive experimental
studies are conducted on benchmark datasets to validate the effectiveness of
our human motion prediction model.
