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SimVP: Simpler yet Better Video Prediction

Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li

arXiv:2206.05099v1cs.CVcs.AI

TL;DR

Video prediction has become increasingly complex, raising the question of whether simpler models can perform comparably. SimVP uses a fully CNN-based encoder-translator-decoder trained end-to-end with MSE loss, and reports state-of-the-art results without complex modules or strategies. Its simplicity and reduced computing cost support use as a baseline and scaling to more scenarios.

  • Problem

    Increasing model complexity makes performance gains harder to understand and scaling to large datasets intractable, motivating the search for a simpler video prediction model.

  • Method

    SimVP is a simple video prediction network with a CNN encoder, translator, and decoder, trained end-to-end using MSE loss.

  • Results

    SimVP achieves state-of-the-art results without complex modules, strategies, or tricks, including on five benchmark datasets.

  • Takeaways & Limitations

    SimVP provides a strong, easy-to-use baseline, while its reduced computing cost makes scaling to more scenarios easier.

  • Takeaways & Limitations

    CNN-based methods may be difficult to scale to prediction with flexible length.

Abstract

from arXiv · show

From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. We admire these progresses but are confused about the necessity: is there a simple method that can perform comparably well? This paper proposes SimVP, a simple video prediction model that is completely built upon CNN and trained by MSE loss in an end-to-end fashion. Without introducing any additional tricks and complicated strategies, we can achieve state-of-the-art performance on five benchmark datasets. Through extended experiments, we demonstrate that SimVP has strong generalization and extensibility on real-world datasets. The significant reduction of training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to stimulate the further development of video prediction. The code is available at \href{https://github.com/gaozhangyang/SimVP-Simpler-yet-Better-Video-Prediction}{Github}.

1. Introduction

Video prediction has advanced through increasingly elaborate architectures and training strategies, motivating the question of whether a simpler model can achieve strong performance. SimVP addresses this question with a fully CNN-based model trained end-to-end by MSE loss, reporting state-of-the-art performance on five benchmark datasets.

  • Motivation: Video prediction methods increasingly use novel neural operators, elaborate architectures, and specialized training strategies to handle spatio-temporal complexity.These approaches include RNNs, transformers, autoregressive models, and normalizing flows.
  • Existing architectures: The field groups representative methods into RNN-RNN-RNN, CNN-RNN-CNN, CNN-ViT-CNN, and CNN-CNN-CNN frameworks.RNN models have been favored since 2014, while the figure contrasts how architectures learn temporal evolution and spatial dependency.
  • Motivation: Purely CNN-based models are less favored, and they commonly rely on adversarial training, teacher-student distillation, or optical flow for improvement.This motivates examining how far a simple CNN model can go without such additions.
  • SimVP: SimVP is fully based on CNN, trained with MSE loss end-to-end, and achieves state-of-the-art performance on five benchmark datasets without additional tricks or complex strategies.The authors present its simplicity as making the model easy to understand and use as a common baseline.

2. Background

Video prediction maps past frames to future frames, while prior approaches use recurrent, transformer, and other specialized architectures. SimVP is motivated by the difficulty of understanding and scaling increasingly complex models and instead uses a simpler CNN-based design.

  • Problem formulation: Video prediction infers future frames from previous frames by learning a mapping F_Θ from an input sequence X_t,T to a future sequence Y_t,T′.Each frame is represented with C channels, height H, and width W.
  • Problem formulation: The predicting model is optimized with a loss function, and SimVP simply employs MSE loss in its setting.The optimization learns parameters Θ for the mapping from past to future frames.
  • Prior frameworks: RNN-RNN-RNN methods stack recurrent networks and design specialized recurrent modules or architectures to capture spatio-temporal correlations and long-term dependencies.Examples include ConvLSTM, PredRNN, PredRNN++, MIM-LSTM, and dGRU.
  • Prior frameworks: CNN-RNN-CNN methods encode frames into latent space, use RNNs to predict future latent states, and focus on modifying recurrent and encoding-decoding modules.Examples incorporate ConvLSTM, optical flow, variational generation, and 3D convolutions.
  • Prior frameworks: CNN-ViT-CNN methods introduce vision transformers to model latent video dynamics, but video-prediction applications of ViT remain limited.Related approaches use temporal or space-time attention and latent-space prediction.
  • Prior frameworks: The CNN-CNN-CNN framework is simple but has been less popular because complex modules and training strategies are usually added to improve novelty and performance.The paper asks whether a much simpler solution can match or exceed state-of-the-art performance.
  • Motivation: Increasing model complexity makes performance gains harder to understand and scaling to large datasets intractable, motivating a simple network built from existing CNNs.The paper explicitly does not propose new modules.

3. SimVP

SimVP uses a CNN encoder, translator, and decoder to separate spatial feature extraction, temporal evolution learning, and future-frame reconstruction. Its design uses standard CNN blocks, Inception modules, shortcuts, and vanilla MSE loss rather than RNNs, transformers, or complex training strategies.

  • Overall framework: SimVP consists of a CNN-based encoder, translator, and decoder that extract spatial features, learn temporal evolution, and predict future frames.The three components integrate spatio-temporal information through the prediction pipeline.
  • Encoder: The encoder stacks N_s ConvNormReLU blocks to extract spatial features by convoluting C channels over spatial dimensions (H, W).Each block uses Conv2d, LayerNorm, and LeakyReLU, mapping input features z_i−1 to hidden features z_i.
  • Translator: The translator uses N_t Inception modules to learn temporal evolution by convoluting T × C channels over (H, W).Each module combines a 1×1 bottleneck Conv2d with parallel GroupConv2d operators.
  • Decoder: The decoder uses N_s unConvNormReLU blocks to reconstruct ground-truth frames by convoluting C channels over (H, W).The decoder blocks use ConvTranspose2d, GroupNorm, and LeakyReLU; ConvTranspose2d serves as the unConv2d operator.
  • Design principle: SimVP avoids advanced modules such as RNN, LSTM, and Transformer, using CNNs, shortcuts, and vanilla MSE loss instead.The design does not introduce complex training strategies such as adversarial training or curriculum learning.

4. Experiments

Experiments evaluate SimVP across five datasets and common prediction metrics, comparing its performance, efficiency, translator choices, generalization, and flexible-length forecasting. The results show strong benchmark performance and efficiency, with CNN-based translation offering robust behavior and cross-dataset and long-horizon extensibility.

  • Experimental setup: Experiments use MSE, MAE, SSIM, PSNR, per-epoch running time, and per-sample memory footprint across five datasets.These measures provide both prediction-quality and computational comparisons.
  • Benchmark performance: SimVP achieves state-of-the-art MSE and SSIM on Moving MNIST, while reported MSE reductions reach 42% over previous methods.The comparison uses reported results under common dataset, metric, and protocol conditions.
  • Computational efficiency: SimVP trains much faster than competing methods because CNN computation is optimized and avoids iterative calculation.The efficiency comparison reports memory, FLOPs, and total training time on Moving MNIST.
  • Translator selection: On Moving MNIST and Human3.6, translator behavior differs: SimVP converges faster early, while CrevNet performs better later; SimVP significantly outperforms others on Human3.6.The translators are compared with shared encoder and decoder settings and similar GPU memory footprints.
  • Translator selection: CNN and RNN achieve state-of-the-art performance under limited computation, while CNN training is more robust at large learning rates and Transformers show no advantage under similar resources.The study reports that CNN training fluctuates less dramatically than RNN training at large learning rates.
  • Generalization and extensibility: SimVP generalizes across KITTI and Caltech, reducing MSE by 3.1%, improving SSIM by 1.4% and PSNR by 13.0%, with training completed within 4h.Generated objects still have room for improved clarity, although the evaluated metrics exceed previous methods.
  • Generalization and extensibility: For flexible prediction lengths, SimVP achieves state-of-the-art performance, with PSNR improving by 11.8% for 10 →20 and 19.5% for 10 →40.The authors report less performance degradation than other methods on long-term prediction tasks.

5. Ablation study

The ablation study examines architectural shortcuts, normalization, convolutional kernels, model capacity, and the roles of SimVP’s encoder, translator, and decoder. Results identify group convolution and larger kernels as especially influential, while assigning distinct functions to the three modules.

  • Architectural design: Group convolution provides the largest performance gain, followed by group normalization, S-UNet, and T-UNet with approximately similar significance.The reported order is group convolution > group normalization ≈ S-UNet ≈ T-UNet.
  • Convolutional kernel: Larger convolutional kernels and more model parameters lead to better performance in the ablation study.Increasing kernel size improves performance, and doubling the hidden dimension further enhances model 8.
  • Convolutional kernel: SimVP uses multi-scale kernels while keeping the Translator’s parameters at 84% of model 9.Model 9 is formed by doubling model 8’s hidden dimension; SimVP instead selects multi-scale kernels.
  • Module roles: The Translator predicts object position and content, the Decoder optimizes foreground shape, and the Encoder removes background error through spatial UNet connections.The module roles are evaluated by mixing submodules trained for different numbers of epochs and examining the final prediction.

6. Conclusion

SimVP is presented as a simple CNN video-prediction model that achieves state-of-the-art results without complex modules, strategies, or tricks. Its reduced computing cost supports scaling to more scenarios and its use as a baseline for future research.

  • SimVP achieves state-of-the-art video-prediction results without introducing complex modules, strategies, or tricks.
  • Reduced computing cost makes SimVP easier to scale to more scenarios and supports its use as a strong baseline.

7. Appendix

The appendix documents benchmark datasets, prediction protocols, training/testing splits, normalization details, and searched network hyperparameters. It covers synthetic motion, traffic, human activity, and driving-video settings.

  • Dataset: Moving MNIST contains two independently moving digits in a 64 × 64 grid, with models predicting 10 future frames from 10 observed frames.Different initial locations and velocities produce infinitely many length-20 sequences.
  • Dataset: TrafficBJ contains Beijing taxicab GPS trajectories with inflow and outflow channels, using the final four weeks for testing and max-min normalization to [0, 1].After normalization, reported MSE and MAE are 1/4 and 1/2 of their original-data values, respectively.
  • Dataset: Human3.6 records 3.6 million samples of human activities; experiments use walking videos and predict four future RGB frames from four previous frames.
  • Dataset: KITTI and Caltech Pedestrian provide driving-video settings, with training on KITTI and evaluation on Caltech after a 10-frame warm-up.KITTI includes multiple sensor modalities, while Caltech contains approximately 10 hours of 640 × 480 video at 30 FPS.
  • Dataset: KTH contains 25 people performing six actions, using persons 1–16 for training and 17–25 for testing.Models predict the next 20 or 40 frames from 10 observations.
  • Network structure: The appendix defines Ns and Cs as the spatial Encoder or Decoder’s layer number and hidden dimension, respectively, and uses NNI for hyperparameter search.The search space and final dataset-specific settings are reported in Tables 9 and 10.
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