15,133 research outputs found
Video Synthesis from the StyleGAN Latent Space
Generative models have shown impressive results in generating synthetic images. However, video synthesis is still difficult to achieve, even for these generative models. The best videos that generative models can currently create are a few seconds long, distorted, and low resolution. For this project, I propose and implement a model to synthesize videos at 1024x1024x32 resolution that include human facial expressions by using static images generated from a Generative Adversarial Network trained on the human facial images. To the best of my knowledge, this is the first work that generates realistic videos that are larger than 256x256 resolution from single starting images. This model improves the video synthesis in both quantitative and qualitative ways compared to two state-of-the-art models: TGAN and MocoGAN. In a quantitative comparison, this project reaches a best Average Content Distance (ACD) score of 0.167, as compared to 0.305 and 0.201 of TGAN and MocoGAN, respectively
Stochastic Dynamics for Video Infilling
In this paper, we introduce a stochastic dynamics video infilling (SDVI)
framework to generate frames between long intervals in a video. Our task
differs from video interpolation which aims to produce transitional frames for
a short interval between every two frames and increase the temporal resolution.
Our task, namely video infilling, however, aims to infill long intervals with
plausible frame sequences. Our framework models the infilling as a constrained
stochastic generation process and sequentially samples dynamics from the
inferred distribution. SDVI consists of two parts: (1) a bi-directional
constraint propagation module to guarantee the spatial-temporal coherence among
frames, (2) a stochastic sampling process to generate dynamics from the
inferred distributions. Experimental results show that SDVI can generate clear
frame sequences with varying contents. Moreover, motions in the generated
sequence are realistic and able to transfer smoothly from the given start frame
to the terminal frame. Our project site is
https://xharlie.github.io/projects/project_sites/SDVI/video_results.htmlComment: Winter Conference on Applications of Computer Vision (WACV 2020
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