Cross-Axis Feature Fusion with Joint-Wise Motion Difference Prediction for Text-Based 3D Human Motion Editing

1. 1Build a dataset of 3D human motions with corresponding text instructions
2. 2Run a diffusion model to generate edited motions from source motions and text instructions
3. 3Configure a joint-wise motion difference prediction module to preserve motion style and structure
4. 4Test the cross-axis feature fusion approach on the MotionFix dataset
5. 5Apply the trained model to new text-based editing tasks

Researchers and engineers in computer vision and AI can benefit from this technique to improve text-based 3D human motion editing, while data scientists and software engineers can apply this to related projects

💡 Fusing cross-axis features with joint-wise motion difference prediction preserves motion style and structure in text-based 3D human motion editing

💡 Enhance text-based 3D human motion editing with cross-axis feature fusion and joint-wise motion difference prediction