MoCoGAN: Decomposing Motion and Content for Video Generation (1707.04993v2)

Abstract: 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.

• The paper introduces a novel framework that decouples motion and content for effective video generation.
• It details a comprehensive network architecture using deconvolution, convolution, and 3D convolution layers to generate and discriminate images and videos.
• Qualitative results demonstrate the model's ability to maintain subject identity across varying actions, highlighting its potential for applications in virtual media and entertainment.

MoCoGAN: Decomposing Motion and Content for Video Generation - Supplementary Material

Overview

The supplementary material for MoCoGAN: Decomposing Motion and Content for Video Generation provides additional details on network architecture and qualitative results, further elucidating the capabilities and design of the MoCoGAN framework. This ancillary document is crucial for understanding the structural intricacies and performance nuances of the model detailed in the main paper.

Network Architecture

The architecture for MoCoGAN comprises several distinct components tailored for image and video generation and discrimination. Specifically, the document elaborates on three network configurations:

1. Image Generative Network ($G_{\mathrm{I}}$):
   • Accepts a random content vector $\mathbf{z}_{\mathrm{C}}$ sampled from a Gaussian distribution $\mathcal{N}(\mathbf{0}, \mathbf{I})$ and a motion vector $\mathbf{z}_{\mathrm{M}}$ sampled from a probability distribution $R_{\mathrm{M}}$.
   • Consists of a series of deconvolutional (DCONV) layers interleaved with Batch Normalization (BN) and LeakyReLU activations. The network sequentially expands the spatial dimensions to generate frames.
2. Image Discriminative Network ($D_{\mathrm{I}}$):
   • Operates on single images with an input dimensionality of height × width × 3 (color channels).
   • Incorporates convolutional (CONV) layers with progressively deeper feature extraction, followed by Batch Normalization and LeakyReLU, culminating in a Sigmoid activation to ascertain the authenticity of the generated images.
3. Video Discriminative Network ($D_{\mathrm{V}}$):
   • Evaluates sequences of video frames with input dimensions as 16 × height × width × 3.
   • Utilizes 3D convolutional (CONV3D) layers to capture spatio-temporal features, augmented with Batch Normalization and LeakyReLU, concluding with a Sigmoid activation for video realism assessment.

Additional Qualitative Results

To validate the model's capacity for video generation, the supplementary material presents illustrative results through Figures~\ref{supplement-static-faces} and \ref{supplement-static-actions}. These results demonstrate two primary use cases:

1. Facial Expression Generation (Figure~\ref{supplement-static-faces}):
   • Each set of three rows maintains a fixed content vector $\mathbf{z}_\mathrm{C}$ while varying action vector $\mathbf{z}_\mathrm{A}$ and motion vectors $\mathbf{z}_\mathrm{M}^{(t)}$.
   • The findings indicate that MoCoGAN effectively preserves the facial identity throughout the generated video frames, even as the motion varies with time.
2. Human Action Generation (Figure~\ref{supplement-static-actions}):
   • Similar to facial expressions, action generation employs a fixed content vector $\mathbf{z}_\mathrm{C}$ with diverse motion and action vectors.
   • Generated videos consistently retain the identity of the human subject while showcasing a variety of actions, thus proving the model's ability to generalize temporally beyond the training dataset.

Implications and Future Directions

The extended insights into MoCoGAN’s architecture and qualitative performance highlight several significant implications:

• Practical Impact: This model has immediate applications in domains requiring robust video synthesis, such as virtual reality, film production, and video game development, where synthetic and controllable video content is valuable.
• Theoretical Advancements: MoCoGAN contributes to the broader understanding of decomposing motion and content in generative adversarial frameworks. Its architecture and approach underscore the importance of disentangling different generative factors to improve model interpretability and control.
• Future Research: Advancements may focus on enhancing temporal coherence and realism, particularly for longer video sequences. Further exploration could also involve integrating more sophisticated motion models or adopting attention mechanisms to better handle complex scenes and actions.

In conclusion, the supplementary material effectively complements the primary paper, offering detailed architecture information and strong qualitative results that corroborate MoCoGAN's efficacy in video generation tasks.