Given a still photograph, one can imagine how dynamic objects might move against a static background. This idea has been actualized in the form of cinemagraphs, where the motion of particular objects within a still image is repeated, giving the viewer a sense of animation. In this paper, we learn computational models that can generate cinemagraph sequences automatically given a single image. To generate cinemagraphs, we explore combining generative models with a recurrent neural network and deep Q-networks to enhance the power of sequence generation. To enable and evaluate these models we make use of two datasets, one synthetically generated and the other containing real video generated cinemagraphs. Both qualitative and quantitative evaluations demonstrate the effectiveness of our models on the synthetic and real datasets.
Video frame generation: As a step beyond creating realistic images, video generation requires the model to incorporate temporal information into the generation process. Therefore, video generation models usually generate frames conditioned on the previous frame (or several previous frames), rather than generating from noise, as is usually done in image generation tasks. Some related approaches @cite_0 @cite_23 train generative models either to reconstruct input video frames or to generate the next few consecutive frames, learning representations in an unsupervised framework. Mathieu al @cite_2 combines a mean square error (MSE) loss with an adversarial loss to produce high-quality generation results. Vondrick al @cite_10 proposed a GAN with a spatio-temporal convolutional architecture to learn a scene's foreground and background simultaneously. Zhou al @cite_17 incorporated a recurrent neural network with a generative model to generate frames showing future object states in timelapse videos. Inspired by their work, we apply an LSTM @cite_16 to encode temporal information during cinemagraph generation.
@cite_0 @cite_23 @cite_2 @cite_16 @cite_10 @cite_17
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Generating videos from text has proven to be a significant challenge for existing generative models. We tackle this problem by training a conditional generative model to extract both static and dynamic information from text. This is manifested in a hybrid framework, employing a Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN). The static features, called "gist," are used to sketch text-conditioned background color and object layout structure. Dynamic features are considered by transforming input text into an image filter. To obtain a large amount of data for training the deep-learning model, we develop a method to automatically create a matched text-video corpus from publicly available online videos. Experimental results show that the proposed framework generates plausible and diverse videos, while accurately reflecting the input text information. It significantly outperforms baseline models that directly adapt text-to-image generation procedures to produce videos. Performance is evaluated both visually and by adapting the inception score used to evaluate image generation in GANs. We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene's foreground from the background. Experiments suggest this model can generate tiny videos up to a second at full frame rate better than simple baselines, and we show its utility at predicting plausible futures of static images. Moreover, experiments and visualizations show the model internally learns useful features for recognizing actions with minimal supervision, suggesting scene dynamics are a promising signal for representation learning. We believe generative video models can impact many applications in video understanding and simulation. We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene's foreground from the background. Experiments suggest this model can generate tiny videos up to a second at full frame rate better than simple baselines, and we show its utility at predicting plausible futures of static images. Moreover, experiments and visualizations show the model internally learns useful features for recognizing actions with minimal supervision, suggesting scene dynamics are a promising signal for representation learning. We believe generative video models can impact many applications in video understanding and simulation. Automatic generation of video caption is a challenging task as video is an information-intensive media with complex variations. Most existing methods, either based on language templates or sequence learning, have treated video as a flat data sequence while ignoring intrinsic multimodality nature. Observing that different modalities (e.g., frame, motion, and audio streams), as well as the elements within each modality, contribute differently to the sentence generation, we present a novel deep framework to boost video captioning by learning Multimodal Attention Long-Short Term Memory networks (MA-LSTM). Our proposed MA-LSTM fully exploits both multimodal streams and temporal attention to selectively focus on specific elements during the sentence generation. Moreover, we design a novel child-sum fusion unit in the MA-LSTM to effectively combine different encoded modalities to the initial decoding states. Different from existing approaches that employ the same LSTM structure for different modalities, we train modality-specific LSTM to capture the intrinsic representations of individual modalities. The experiments on two benchmark datasets (MSVD and MSR-VTT) show that our MA-LSTM significantly outperforms the state-of-the-art methods with 52.3 BLEU@4 and 70.4 CIDER-D metrics on MSVD dataset, respectively.