Long-term human motion can be represented as a series of motion modes---motion sequences that capture short-term temporal dynamics---with transitions between them. We leverage this structure and present a novel Motion Transformation Variational Auto-Encoders (MT-VAE) for learning motion sequence generation. Our model jointly learns a feature embedding for motion modes (that the motion sequence can be reconstructed from) and a feature transformation that represents the transition of one motion mode to the next motion mode. Our model is able to generate multiple diverse and plausible motion sequences in the future from the same input. We apply our approach to both facial and full body motion, and demonstrate applications like analogy-based motion transfer and video synthesis.
Besides the flow representation, motion synthesis has been explored in a broader context, namely, video generation. For example, synthesizing video sequence in the future from a single or multiple video frames as initialization. Early works employed patch-based method for short-term video generation using mean squared mean squared loss @cite_46 or perceptual loss @cite_27 . Given an atomic action as additional condition, previous works extended with action-conditioned (i.e., rotation, location, etc) architectures that enable better semantic control in video generation @cite_8 @cite_59 @cite_31 @cite_57 . Due to the difficulty in holistic video frame prediction, the idea of disentangling video factors into motion and content is explored in @cite_44 @cite_4 @cite_9 @cite_54 @cite_2 @cite_11 . Video generation has also been approached with architectures that output multinomial distribution vectors over the possible pixel values for each pixel in the generated frame @cite_49 .
@cite_31 @cite_4 @cite_8 @cite_9 @cite_54 @cite_57 @cite_44 @cite_27 @cite_59 @cite_2 @cite_49 @cite_46 @cite_11
2883938080 2898823814 2883991972 2886748926
Due to the emergence of Generative Adversarial Networks, video synthesis has witnessed exceptional breakthroughs. However, existing methods lack a proper representation to explicitly control the dynamics in videos. Human pose, on the other hand, can represent motion patterns intrinsically and interpretably, and impose the geometric constraints regardless of appearance. In this paper, we propose a pose guided method to synthesize human videos in a disentangled way: plausible motion prediction and coherent appearance generation. In the first stage, a Pose Sequence Generative Adversarial Network (PSGAN) learns in an adversarial manner to yield pose sequences conditioned on the class label. In the second stage, a Semantic Consistent Generative Adversarial Network (SCGAN) generates video frames from the poses while preserving coherent appearances in the input image. By enforcing semantic consistency between the generated and ground-truth poses at a high feature level, our SCGAN is robust to noisy or abnormal poses. Extensive experiments on both human action and human face datasets manifest the superiority of the proposed method over other state-of-the-arts. Existing video frame synthesis works suffer from improving perceptual quality and preserving semantic representation ability. In this paper, we propose a Progressive Motion-texture Synthesis Network (PMSN) to address this problem. Instead of learning synthesis from scratch, we introduce augmented inputs to compensate texture details and motion information. Specifically, a coarse-to-fine guidance scheme with a well-designed semantic loss is presented to improve the capability of video frame synthesis. As shown in the experiments, our proposed PMSN promises excellent quantitative results, visual effects, and generalization ability compared with traditional solutions. Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame. In this work, we study the problem of generating consecutive multiple future frames by observing one single still image only. We formulate the multi-frame prediction task as a multiple time step flow (multi-flow) prediction phase followed by a flow-to-frame synthesis phase. The multi-flow prediction is modeled in a variational probabilistic manner with spatial-temporal relationships learned through 3D convolutions. The flow-to-frame synthesis is modeled as a generative process in order to keep the predicted results lying closer to the manifold shape of real video sequence. Such a two-phase design prevents the model from directly looking at the high-dimensional pixel space of the frame sequence and is demonstrated to be more effective in predicting better and diverse results. Extensive experimental results on videos with different types of motion show that the proposed algorithm performs favorably against existing methods in terms of quality, diversity and human perceptual evaluation. We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video. While its image counterpart, the image-to-image synthesis problem, is a popular topic, the video-to-video synthesis problem is less explored in the literature. Without understanding temporal dynamics, directly applying existing image synthesis approaches to an input video often results in temporally incoherent videos of low visual quality. In this paper, we propose a novel video-to-video synthesis approach under the generative adversarial learning framework. Through carefully-designed generator and discriminator architectures, coupled with a spatio-temporal adversarial objective, we achieve high-resolution, photorealistic, temporally coherent video results on a diverse set of input formats including segmentation masks, sketches, and poses. Experiments on multiple benchmarks show the advantage of our method compared to strong baselines. In particular, our model is capable of synthesizing 2K resolution videos of street scenes up to 30 seconds long, which significantly advances the state-of-the-art of video synthesis. Finally, we apply our approach to future video prediction, outperforming several state-of-the-art competing systems.