The VideoSwap framework introduces semantic point correspondence, enabling the replacement of shape changes between source and target video subjects. The new model uses semantic point correspondence to overcome the limitations of traditional video editing when dealing with shape transformation. Researchers validated through human evaluations that VideoSwap outperforms other methods in subject identity, action alignment, and temporal consistency. This project showcases the latest achievements in custom video subject swapping, bringing innovation to the field of video editing.