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In this paper, we propose to model the video dynamics by learning the trajectory of independently inverted latent codes from GANs.
A family of embedded runge-kutta formulae
John R Dormand and Peter J Prince · 1980
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Poisson image editing
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Image quality assessment: From error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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The Caltech-UCSD Birds-200-2011 Dataset
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Adam: A method for stochastic optimization
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Phase-based frame interpolation for video
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Video frame synthesis using deep voxel flow
Ziwei Liu, Raymond A Yeh, Xiaoou Tang, Yiming Liu, and Aseem Agarwala · 2017
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Video frame interpolation via adaptive separable convolution
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Neural ordinary differential equations
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The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english
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MoCoGAN: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
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Towards accurate generative models of video: A new metric & challenges
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Simple video generation using neural ODEs
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SinGAN: Learning a generative model from a single natural image
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Vid-ode: Continuous-time video generation with neural ordinary differential equation
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StyleCLIP: Text-driven manipulation of StyleGAN imagery
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Encoding in style: a StyleGAN encoder for image-to-image translation
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TediGAN: Text-guided diverse face image generation and manipulation
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World-consistent video-to-video synthesis
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Semi-supervised StyleGAN for disentanglement learning
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Restyle: A residual-based stylegan encoder via iterative refinement
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From continuity to editability: Inverting gans with consecutive images
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A latent transformer for disentangled face editing in images and videos
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High-fidelity GAN inversion with padding space
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GAN inversion: A survey
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