MoCoGAN: Decomposing Motion and Content for Video Generation

Visual signals in a video can be divided into content and motion. While content specifies which objects are in the video, motion describes their dynamics. Based on this prior, we propose the Motion and Content decomposed Generative Adversarial Network (MoCoGAN) framework for video generation. The proposed framework generates a video by mapping a sequence of random vectors to a sequence of video frames. Each random vector consists of a content part and a motion part. While the content part is kept fixed, the motion part is realized as a stochastic process. To learn motion and content decomposition in an unsupervised manner, we introduce a novel adversarial learning scheme utilizing both image and video discriminators. Extensive experimental results on several challenging datasets with qualitative and quantitative comparison to the state-of-the-art approaches, verify effectiveness of the proposed framework. In addition, we show that MoCoGAN allows one to generate videos with same content but different motion as well as videos with different content and same motion.

PDF Abstract CVPR 2018 PDF CVPR 2018 Abstract
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Video Generation BAIR Robot Pushing MoCoGAN FVD score 503 # 28
Cond 4 # 30
Pred 12 # 1
Train 12 # 17
Video Generation UCF-101 16 frames, 64x64, Unconditional MoCoGAN Inception Score 12.42 # 3
Video Generation UCF-101 16 frames, Unconditional, Single GPU MoCoGAN Inception Score 12.42 # 4


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