Source-linked AI summary
Generating Videos with Scene Dynamics
Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
TL;DR
Learning scene dynamics from unlabeled video is difficult but valuable for video recognition and generation. The paper extends generative adversarial networks with a two-stream foreground-background architecture, and experiments support plausible video dynamics, useful action-recognition features, and occasional plausible futures from static images.
Problem
Learning scene dynamics for video recognition and generation is challenging because scene changes have many possibilities and annotations are expensive and ambiguous.
Method
The paper extends generative adversarial networks to video with a two-stream model that explicitly separates moving foreground from static background.
Results
Experiments support plausible generated motions, occasional plausible futures from static images, and useful action-recognition features learned without supervision.
Takeaways & Limitations
Abundant unlabeled video is a promising source of scene-dynamics priors for video generation and visual representation learning.
Takeaways & Limitations
Generated futures are often plausible but usually not the correct video, with scene changes including altered colors or dropped or hallucinated objects.
Abstract
from arXiv · showhide
We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene's foreground from the background. Experiments suggest this model can generate tiny videos up to a second at full frame rate better than simple baselines, and we show its utility at predicting plausible futures of static images. Moreover, experiments and visualizations show the model internally learns useful features for recognizing actions with minimal supervision, suggesting scene dynamics are a promising signal for representation learning. We believe generative video models can impact many applications in video understanding and simulation.
1 Introduction
The paper learns scene dynamics from large-scale unlabeled video for recognition and generation. Its experiments support plausible video generation, future prediction from images, and useful action-recognition representations.
- Scene dynamics are central to both action classification and future video prediction, but the many possible scene changes make them difficult to model.
- The paper learns temporal knowledge directly from large amounts of unlabeled, in-the-wild video rather than relying on expensive and ambiguous annotations.Unlabeled video can be acquired at massive scale and provides temporal signals through frame coherence.
- The proposed two-stream generative model separates foreground from background and enforces a stationary background to learn object motion.The model extends generative adversarial networks to video and uses explicit foreground-background separation.
- Experiments found plausible generated motions, higher human preference for the full model, sometimes plausible futures from static images, and useful unsupervised action-recognition features.Visualizations also suggest future generation may provide a supervisory signal for recognizing objects in motion.
- The paper’s primary contribution is using unlabeled video to acquire priors about scene dynamics, with generative video models proposed for simulation, forecasting, and representation learning.
2 Generative Models for Video
The video model extends generative adversarial networks with spatio-temporal generation and an explicit foreground-background decomposition. A two-stream generator combines moving foreground content with a stationary background while a spatio-temporal discriminator evaluates appearance and motion.
- 2.1 Review: Generative Adversarial Networks: The model trains a generator to produce videos and a discriminator to distinguish real videos from generated ones in an adversarial min-max game.The generator and discriminator are optimized with gradient-based methods.
- 2.2 Generator Network: The generator maps a low-dimensional latent code z to a high-dimensional video, with z typically sampled from a simple distribution such as a Gaussian.
- 2.2 Generator Network: The architecture targets spatial and temporal translation invariance, efficient upsampling, and stationary-camera scenes where objects move against a static background.
- 2.2 Generator Network: The one-stream generator uses spatio-temporal convolutions for video invariance and fractionally strided convolutions to upsample from a low-dimensional input.
- 2.2 Generator Network: The two-stream architecture explicitly separates a moving foreground from a static background, combining them with a spatio-temporal mask.The background stream produces an image replicated over time, while the foreground stream produces a masked spatio-temporal cuboid.
- 2.2 Generator Network: The generator produces 64 × 64 videos for 32 frames, slightly more than one second, using fractionally strided convolutional networks for the mask, foreground, and background.
- 2.3 Discriminator Network: The discriminator uses five-layer spatio-temporal convolutions to recognize both realistic scenes and realistic motion between frames.
3 Experiments
Experiments evaluate VGAN for video generation, representation learning, and future prediction using qualitative, psychophysical, and action-classification tests. The model produces plausible scene motions, benefits from its two-stream design, and transfers useful features to recognition with limited labels.
- Video Generation: Generated scenes are generally sharp and exhibit motion patterns appropriate to their scene categories, although moving objects often lack resolution.Examples include crashing waves, people walking on grass, and trains moving along tracks; people may appear as blobs.
- Video Generation: The two-stream model explicitly separates foreground, background, and masks, and visualizations show that it generally disentangles moving objects from stationary backgrounds.The architecture is designed to keep the background stationary while modeling foreground motion.
- Video Generation: Workers consistently preferred GAN-generated videos over autoencoder generations and slightly preferred the two-stream architecture, especially for scenes with large backgrounds.Workers chose static frames only 38% of the time, while the two-stream model produced less background flickering and was most difficult to distinguish from real videos.
- Experimental Setup: The study uses unlabeled Flickr video for generation and representation learning, with filtered scene-category data for evaluating generated videos.The representation-learning dataset contains over 5,000 hours of unfiltered video; generation experiments use filtered categories and stabilized camera motion.
- Video Representation Learning: Fine-tuning the discriminator on UCF101 action classification outperforms random initialization and STIP features, while slightly exceeding another unsupervised representation with fewer parameters and lower-resolution videos.With one eighth of the labeled data, the fine-tuned model still beats a randomly initialized network.
- Future Generation: Given a single static image, future-generation outputs are rarely correct but often contain plausible motions, with scene changes such as altered colors or hallucinated objects as common failures.The experiment demonstrates an application of generative video models while exposing limitations in preserving the input scene.
- Conclusion: The conclusion argues that abundant unlabeled video supports both video generation and visual-representation learning, while noting that fully exploiting it remains an open challenge.The paper frames generative video models as relevant to simulations, forecasting, and representation learning.