Latent Video Transformer

• URL: http://arxiv.org/abs/2006.10704v1
• Date: Thu, 18 Jun 2020 17:38:38 GMT
• Title: Latent Video Transformer
• Authors: Ruslan Rakhimov, Denis Volkhonskiy, Alexey Artemov, Denis Zorin, Evgeny Burnaev
• Abstract summary: Some generative models for videos require up to 512 Processing Units for parallel training. In this work, we address this problem via modeling the dynamics in a latent space. We demonstrate the performance of our approach on BAIR Robot Pushing Kinetics-600 datasets.
• Score: 30.0340468756089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models [71.9811050853964]
  VideoJAM is a novel framework that instills an effective motion prior to video generators. VideoJAM achieves state-of-the-art performance in motion coherence. These findings emphasize that appearance and motion can be complementary and, when effectively integrated, enhance both the visual quality and the coherence of video generation.
  arXiv  Detail & Related papers  (2025-02-04T17:07:10Z)
• Autoregressive Video Generation without Vector Quantization [90.87907377618747]
  We reformulate the video generation problem as a non-quantized autoregressive modeling of temporal frame-by-frame prediction. With the proposed approach, we train a novel video autoregressive model without vector quantization, termed NOVA. Our results demonstrate that NOVA surpasses prior autoregressive video models in data efficiency, inference speed, visual fidelity, and video fluency, even with a much smaller model capacity.
  arXiv  Detail & Related papers  (2024-12-18T18:59:53Z)
• From Slow Bidirectional to Fast Autoregressive Video Diffusion Models [52.32078428442281]
  Current video diffusion models achieve impressive generation quality but struggle in interactive applications due to bidirectional attention dependencies. We address this limitation by adapting a pretrained bidirectional diffusion transformer to an autoregressive transformer that generates frames on-the-fly. Our model achieves a total score of 84.27 on the VBench-Long benchmark, surpassing all previous video generation models.
  arXiv  Detail & Related papers  (2024-12-10T18:59:50Z)
• Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction [43.16308241800144]
  We introduce a novel model class, that treats video as a continuous multi-dimensional process rather than a series of discrete frames. We establish state-of-the-art performance in video prediction, validated on benchmark datasets including KTH, BAIR, Human3.6M, and UCF101.
  arXiv  Detail & Related papers  (2024-12-06T10:34:50Z)
• ZeroSmooth: Training-free Diffuser Adaptation for High Frame Rate Video Generation [81.90265212988844]
  We propose a training-free video method for generative video models in a plug-and-play manner. We transform a video model into a self-cascaded video diffusion model with the designed hidden state correction modules. Our training-free method is even comparable to trained models supported by huge compute resources and large-scale datasets.
  arXiv  Detail & Related papers  (2024-06-03T00:31:13Z)
• HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator [90.74663948713615]
  We train an autoregressive latent video prediction model capable of predicting high-fidelity future frames. We produce high-resolution (256x256) videos with minimal modification to existing models.
  arXiv  Detail & Related papers  (2022-09-15T08:41:57Z)
• Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation [14.631523634811392]
  Masked Conditional Video Diffusion (MCVD) is a general-purpose framework for video prediction. We train the model in a manner where we randomly and independently mask all the past frames or all the future frames. Our approach yields SOTA results across standard video prediction benchmarks, with computation times measured in 1-12 days.
  arXiv  Detail & Related papers  (2022-05-19T20:58:05Z)
• Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction [79.23730812282093]
  We introduce Greedy Hierarchical Variational Autoencoders (GHVAEs), a method that learns high-fidelity video predictions by greedily training each level of a hierarchical autoencoder. GHVAEs provide 17-55% gains in prediction performance on four video datasets, a 35-40% higher success rate on real robot tasks, and can improve performance monotonically by simply adding more modules.
  arXiv  Detail & Related papers  (2021-03-06T18:58:56Z)
• Transformation-based Adversarial Video Prediction on Large-Scale Data [19.281817081571408]
  We focus on the task of video prediction, where given a sequence of frames extracted from a video, the goal is to generate a plausible future sequence. We first improve the state of the art by performing a systematic empirical study of discriminator decompositions. We then propose a novel recurrent unit which transforms its past hidden state according to predicted motion-like features.
  arXiv  Detail & Related papers  (2020-03-09T10:52:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.