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Latent Video Transformer

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arxiv 2006.10704 v1 pith:WNFKLQRI submitted 2020-06-18 cs.CV cs.LGeess.IV

Latent Video Transformer

Ruslan Rakhimov , Denis Volkhonskiy , Alexey Artemov , Denis Zorin , Evgeny Burnaev This is my paper
classification cs.CV cs.LGeess.IV
keywords frameslatentmodelsvideoapproachgenerationproblemprocessing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The video generation task can be formulated as a prediction of future video frames given some past frames. Recent generative models for videos face the problem of high computational requirements. Some models require up to 512 Tensor Processing Units for parallel training. In this work, we address this problem via modeling the dynamics in a latent space. After the transformation of frames into the latent space, our model predicts latent representation for the next frames in an autoregressive manner. We demonstrate the performance of our approach on BAIR Robot Pushing and Kinetics-600 datasets. The approach tends to reduce requirements to 8 Graphical Processing Units for training the models while maintaining comparable generation quality.

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Cited by 5 Pith papers

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  3. Latent Video Diffusion Models for High-Fidelity Long Video Generation

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  4. VideoGPT: Video Generation using VQ-VAE and Transformers

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  5. CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

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