Temporal anti-aliasing, also known as motion blur, is the effect caused by the movement of objects during the camera exposure time. Most previous temporal anti-aliasing methods have been based on temporal supersampling. However, supersampling often generates a series of separate objects rather than a blurred image, so called the spaghetti effect. After analyzing the temporal anti-aliasing process, we establish three temporal anti-aliasing equations. These equations have the capability of interpreting previous results as their approximated forms. Based on these temporal anti-aliasing equations, we suggest two new temporal anti-aliasing methods: temporal interpolation method and swept volume-based method. The temporal interpolation method decomposes the temporal anti-aliasing process into two stages: temporal visibility check stage and time-accumulated display stage. We provide approximated solutions for each stage,while an analytical solution is impossible with the current technology. Finally, we show that the temporally anti-aliased image can be approximated by the weighted sum of convolutions of simple basis functions. The swept volume-based method approximates a temporally anti-aliased image as the image of the three- dimensional swept volume of the moving object. Since previous swept volume calculation algorithms are too complex and yet slow for temporal anti- aliasing effects, we provide new swept volume calculation algorithms for simple translational motions and simple rotational motions. Then, the intensity values of the swept volume are faded down based on the convolution results which are approximated along the sweep path.