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High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks
Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee
TL;DR
Future video prediction is difficult because videos contain many sources of spatio-temporal variation, while prior methods often rely on specialized inductive biases. The paper performs a large-scale study of standard, minimally biased models scaled in capacity, finding that large recurrent stochastic models achieve high-quality predictions across object-interaction, human-motion, and car-driving datasets.
Problem
Video prediction is difficult because future frames contain many spatio-temporal factors of variation, and existing approaches often use specialized computations such as optical flow or segmentation.
Method
The paper studies standard stochastic recurrent video-prediction networks with minimal inductive bias while scaling encoder, decoder, and LSTM capacity.
Results
Across three datasets, large models improve video-prediction quality; recurrence helps over non-recurrent models, and stochasticity helps especially when future actions or controls are uncertain.
Takeaways & Limitations
High-quality video prediction can be obtained without optical flow, segmentation masks, adversarial losses, landmarks, or other specialized inductive biases.
Abstract
from arXiv · showhide
Predicting future video frames is extremely challenging, as there are many factors of variation that make up the dynamics of how frames change through time. Previously proposed solutions require complex inductive biases inside network architectures with highly specialized computation, including segmentation masks, optical flow, and foreground and background separation. In this work, we question if such handcrafted architectures are necessary and instead propose a different approach: finding minimal inductive bias for video prediction while maximizing network capacity. We investigate this question by performing the first large-scale empirical study and demonstrate state-of-the-art performance by learning large models on three different datasets: one for modeling object interactions, one for modeling human motion, and one for modeling car driving.
1 Introduction
Video prediction is difficult because future frames contain many spatio-temporal variations, yet it is important for agents that learn world models and plan actions. The paper asks whether large models with minimal inductive bias can replace specialized video-prediction architectures.
- Video prediction can support model-based reinforcement learning by providing a learned world model for planning or policy learning.
- Video prediction remains an open problem because videos contain many spatio-temporal factors of variation that neural networks must model.
- The paper investigates whether maximizing capacity with minimal inductive bias is sufficient instead of using specialized video-prediction architectures.
- The study reports that larger minimal-bias models improve performance, recurrent models outperform non-recurrent models, and stochastic models help especially under uncertainty.
2 Related Work
Prior video-prediction work used specialized techniques to model spatial structure and stochastic futures. This paper instead conducts a thorough capacity study using a standard neural network without such specialized computations.
- Earlier video-prediction methods addressed patch artifacts, action conditioning, optical flow, adversarial objectives, and stochastic latent-variable modeling.
- The paper investigates whether high-quality video prediction can be achieved by maximizing the capacity of a standard neural network without optical flow or adversarial objectives.
- The authors describe this as the first thorough investigation of capacity increases for video prediction.
3 Scaling up video prediction
The method scales a stochastic recurrent video-prediction architecture built from standard neural-network layers, then removes stochasticity or recurrence for ablations. Capacity is increased through encoder, decoder, and LSTM width factors.
- The baseline combines a stochastic component, shallow convolutional encoder-decoders, and a convolutional LSTM using standard neural-network layers.
- The model is optimized with a variational lower bound containing a reconstruction term and a KL-divergence term weighted by β.
- During training, the posterior LSTM produces Gaussian latent-code parameters, while the generative LSTM uses encoded frames and sampled latent variables to predict frames.
- Capacity is scaled with K for encoder and decoder widths and M for LSTM widths, increasing both until device limits, with LSTM scaling capped at M = 3.
- Ablations remove the stochastic component or the LSTM, leaving deterministic recurrent or convolutional encoder-decoder variants.
4 Experiments
Experiments evaluate scalable video prediction models on object interactions, human motion, and driving using frame-wise, dynamics-based, and human-preference measures. Across datasets, larger recurrent models generally improve prediction quality, while non-recurrent CNNs remain weak on complex temporal dynamics.
- Experimental setup: The evaluation covers object interactions, structured human motion, partial observability in driving, and five metrics spanning pixels, perceptual similarity, dynamics, and human judgments.The datasets present different prediction challenges, and the evaluation includes PSNR, SSIM, VGG cosine similarity, FVD, and AMT preference.
- Overall evaluation: Larger recurrent models achieve better dynamics-based performance than their smaller counterparts across the evaluated datasets.Table 1 compares the biggest models with baseline models, and reports significantly better results for the biggest recurrent models.
- Robot arm: On towel-pick videos, increasing capacity improves FVD and qualitative sharpness, while CNNs remain worse than recurrent models.The largest SVG’ and LSTM models perform similarly well, and the largest stochastic model preserves sharper object edges than the small baseline.
- Human activities: On Human 3.6M, recurrence and capacity are both important: LSTM and SVG’ improve with scale, whereas CNN performance remains poor.The largest models also receive strong human preference, with 95.8% preferring the larger SVG’ model and 98.7% preferring the larger LSTM model.
- Car driving: On KITTI driving, LSTM and SVG’ perform similarly frame-wise, while the largest recurrent models improve dynamics realism and human preference.The largest SVG’ and LSTM models are each preferred by human raters 99.3% of the time over their baselines.
5 Higher resolution videos
Higher-resolution experiments compare the smallest and largest models on Human 3.6M and KITTI driving videos, finding qualitatively better results for the largest network.
- 5 Higher resolution videos: The higher-resolution setup uses 5 input frames and predicts 10 frames during training, then 25 frames during testing.Three additional convolutional layers plus pooling reduce inputs to the previous encoder output resolution.
- 5 Higher resolution videos: The largest network produces videos that are harder to distinguish from real videos in the 128x128 Human 3.6M and KITTI comparisons.The comparison uses M=3 and K=3 for the largest model.
6 Conclusion
The conclusion reports a broad empirical study of minimal inductive bias and model capacity for video generation, emphasizing recurrence, stochasticity, and scaling.
- 6 Conclusion: Experiments confirm that recurrent connections and stochasticity improve video prediction when future actions or controls are uncertain.The study evaluates these design choices using five different metrics.
- 6 Conclusion: Increasing the capacity of models with minimal inductive bias improves the quality of video prediction.The authors frame this as a direction for pursuing high-quality video prediction.
A.1 Video results
The supplementary video comparisons provide baseline-versus-largest-model examples for the paper’s best LSTM and SVG’ models.
- A.1 Video results: Video comparisons show the baseline and largest versions of the best LSTM and SVG’ models.
A.2 Per-frame evaluation comparison as model capacity increases
The supplementary evaluation examines how performance changes with model capacity for each experimented model.
- A.2 Per-frame evaluation comparison as model capacity increases: Per-frame evaluation is reported across model capacities for each model family studied in the paper.
A.2.1 Robot arm.
Increasing capacity improves per-frame evaluation for towel-pick videos, especially for recurrent LSTM and SVG’ models, with gains attributed to better modeling of robot-arm interactions.
- A.2.1 Robot arm.: LSTM and SVG’ models show more noticeable improvement as parameter count increases, whereas CNN gains are slight.The improvement is hypothesized to reflect greater capacity for handling robot-arm interactions with objects.
- A.2.1 Robot arm.: As capacity increases, per-frame metrics improve because objects become sharper and robot-arm dynamics are modeled better.
A.2.2 Human activities.
Per-frame evaluation is unreliable for Human 3.6M because static background dominates the frames, allowing a last-frame-copy baseline to outperform learned predictors.
- A.2.2 Human activities.: Copying the last observed frame significantly outperforms all models, showing that per-frame evaluation is unreliable when most video pixels remain static.The moving human occupies only a small portion of each frame, so background accuracy dominates the metric.
A.2.3 Car driving.
Car-driving evaluation shows that larger recurrent models improve video-dynamics and per-frame performance, while partial observability makes exact future-frame prediction increasingly difficult over longer horizons.
- A.2.3 Car driving.: As capacity increases, KITTI per-frame metrics improve because larger models better capture driving dynamics and partial observability.Performance converges toward the largest models because exact unseen image regions are difficult to predict.
- A.2.3 Car driving.: Skip connections improve frame-wise accuracy for human-motion videos because moving humans occupy a small portion of the image.Transferring non-moving pixels directly into outputs improves per-frame metrics.
- A.2.3 Car driving.: Recurrent models benefit from skip connections in KITTI video-dynamics evaluation, while CNN gains diminish at larger model sizes.The comparison uses solid lines for skip connections and dotted lines without them.
- A.2.3 Car driving.: Per-frame predictions become less accurate over time, reducing the gap between architectures with and without skip connections.Skip connections help nearby frames, but longer horizons require generating fully unseen objects under partial observability.
- A.2.3 Car driving.: For action-free experiments, recurrent models generally benefit from more context, whereas CNN models often perform better with fewer context frames.The action-free comparison covers Human 3.6M and KITTI and predicts 20 future frames.
- A.2.3 Car driving.: For action-conditioned experiments, 8 context frames are optimal for SVG’ and LSTM video dynamics, while CNN models are best with 2 frames.The action inputs simplify prediction for CNN models by providing motion information directly.