{'fraction_non_alphanumeric': 0.011157601115760111, 'fraction_numerical': 0.0, 'mean_word_length': 5.189655172413793, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Bayesian View of Frame Interpolation and a Comparison with Existing Motion Picture Effects Tools

Frame interpolation is the process of synthesising a new frame in-between existing frames in an image sequence. It has emerged as a key module in motion picture effects. Previous work either relies on two frame interpolation based entirely on optic flow, or recently DNNs. This paper presents a new algorithm based on multiframe motion interpolation motivated in a Bayesian sense. We also present the first comparison using industrial toolkits used in the post production industry today. We find that the latest Convolutional Neural Network approaches do not significantly outperform explicit motion based techniques.