ResFields are specifically designed neural networks for effectively representing complex spatio-temporal signals. By incorporating time-varying weights into the multi-layer perceptron, it enhances the model's expressive power with trainable residual parameters. This method can be seamlessly integrated into existing technologies and significantly improves the results of challenging tasks such as 2D video approximation, dynamic shape modeling, and dynamic NeRF reconstruction.