Source-linked AI summary
Temporal Generative Adversarial Nets with Singular Value Clipping
Masaki Saito, Eiichi Matsumoto, Shunta Saito
TL;DR
Video generation requires representations that capture temporal structure without treating time like a spatial dimension. TGAN separates temporal latent-sequence generation from image synthesis and uses Singular Value Clipping to stabilize WGAN training, producing better-quality videos and frame interpolation.
Problem
Video generation remains difficult because temporal structure differs from spatial structure, while existing approaches may assume static backgrounds.
Method
TGAN uses a temporal generator for frame-level latent variables, an image generator for video synthesis, and Singular Value Clipping for WGAN training.
Results
The experiments report better-quality generated videos, natural frame interpolation, and more stable training than conventional methods.
Takeaways & Limitations
Separating temporal and image generation provides a video representation that captures time series and supports frame interpolation.
Takeaways & Limitations
Singular Value Clipping increases computational cost, though reducing its application frequency can mitigate this cost.
Abstract
from arXiv · showhide
In this paper, we propose a generative model, Temporal Generative Adversarial Nets (TGAN), which can learn a semantic representation of unlabeled videos, and is capable of generating videos. Unlike existing Generative Adversarial Nets (GAN)-based methods that generate videos with a single generator consisting of 3D deconvolutional layers, our model exploits two different types of generators: a temporal generator and an image generator. The temporal generator takes a single latent variable as input and outputs a set of latent variables, each of which corresponds to an image frame in a video. The image generator transforms a set of such latent variables into a video. To deal with instability in training of GAN with such advanced networks, we adopt a recently proposed model, Wasserstein GAN, and propose a novel method to train it stably in an end-to-end manner. The experimental results demonstrate the effectiveness of our methods.
1. Introduction
TGAN addresses video-generation challenges by separating temporal and spatial generation, while Singular Value Clipping stabilizes WGAN training for advanced network structures.
- Unsupervised video learning remains difficult, especially for generating diversified natural videos without assuming static backgrounds.
- Treating temporal and spatial dimensions identically with 3D convolutions can make video generation difficult because time has different characteristics.
- TGAN uses a temporal generator to produce frame-specific latent variables and an image generator to transform them into a video.
- The two-generator representation captures time series and supports a natural extension to frame interpolation.
- Singular Value Clipping removes a sensitive WGAN hyperparameter to stabilize training of networks with advanced structures.
- The experiments show successful training with the proposed method even when conventional methods experience diverging loss.
2. Related work
Prior work spans unsupervised image generation, video representation learning, sequence prediction, and video generation, but existing approaches impose supervision or structural assumptions.
- 2.1. Natural image generation: Unsupervised image-generation studies include graphical models, recurrent or deconvolutional networks, and GAN extensions, with some requiring labels or additional information.
- 2.2. Video recognition and unsupervised learning: Video recognition and unsupervised representation studies use dense trajectories, CNNs, RBMs, optical flow, SIFT, ISA, and deep neural networks.
- 2.2. Video recognition and unsupervised learning: RNN and LSTM models have been used to predict future video sequences while learning spatial and temporal correlations.
- 2.3. Video generation: A prior video generator used 3D deconvolutions but assumed static backgrounds, whereas TGAN decomposes generation into 1D and 2D deconvolutions without that assumption.
3. Temporal Generative Adversarial Nets
TGAN combines Wasserstein GAN training with separate temporal and image generators: temporal latents produce frame-wise inputs, and the image generator produces the video sequence.
- 3.1. GAN and WGAN: GANs train generator and discriminator networks together, while WGAN replaces Jensen-Shannon divergence with Earth Mover’s distance to address training instability.
- 3.2. Wasserstein GAN: WGAN requires a K-Lipschitz discriminator, whose output is an unbounded real value rather than a probability.
- 3.2. Wasserstein GAN: Weight clipping enforces the Lipschitz condition but obscures the relationship between clipping bound c and the effective K.
- 3.3. Temporal GAN: The temporal generator maps one latent variable z0 to T frame-level latent variables, and the image generator maps these variables to a video.
- 3.3. Temporal GAN: The shared latent variable z0 remains constant across time and helps suppress sudden changes in generated actions.
- 3.3. Temporal GAN: The generator uses temporal one-dimensional deconvolutions and an image-generation network that combines two latent inputs before five deconvolutions.
- 3.3. Temporal GAN: The discriminator uses spatio-temporal 3D convolutional layers to evaluate generated videos.
4. Singular Value Clipping
Singular Value Clipping (SVC) replaces fixed weight clipping with layer-wise spectral constraints to satisfy the 1-Lipschitz condition and stabilize WGAN training. It avoids tuning the clipping hyperparameter c, at the cost of additional computation.
- WGAN weight clipping requires tuning c, and its relationship with the discriminator’s K-Lipschitz constant is unknown.This makes approximating Earth Mover’s distance problematic when the desired constraint is K = 1.
- SVC constrains every discriminator function so their Lipschitz constants combine to the desired overall constant, focusing on K = 1.For linear layers, the spectral norm of each weight matrix is constrained to at most one.
- SVC clips singular values larger than one and reconstructs linear and convolutional-layer parameters from the clipped values.Convolutional weights are treated as matrices before applying singular value decomposition.
- SVC also constrains batch-normalization scaling parameters, while leaving ReLU and LeakyReLU layers unchanged when they already satisfy the condition.The method summarizes layer-specific clipping operations for the 1-Lipschitz constraint.
- SVC removes the need for careful tuning of c and empirically stabilizes WGAN training for advanced models.The paper reports successful training where conventional clipping produces unstable loss behavior.
- SVC increases computational cost, but reducing how often it is applied can mitigate this overhead.The reported SVD time is almost the same as forward-backward computation, and the algorithm applies clipping periodically.
5. Applications
TGAN’s latent-space formulation supports frame interpolation and longer sequences, while Conditional TGAN incorporates video labels to improve generated-video quality. The conditional model supplies labels to both generators and the discriminator.
- 5.1. Frame Interpolation: TGAN can generate an intermediate frame between two adjacent frames by interpolating a trajectory in latent image space.The trajectory is represented by the initial latent variable and frame-specific latent variables.
- 5.1. Frame Interpolation: A bilinear filter in the temporal generator’s last layer interpolates the latent trajectory and helps the generator yield long sequences.
- 5.2. Conditional TGAN: Conditional TGAN takes both a video label l and the latent variable z0 as generator inputs.The conditional extension is intended to exploit category labels and improve generated-video quality.
- 5.2. Conditional TGAN: In CTGAN, the temporal generator concatenates a one-hot label vector with z0, while the image generator also receives the label vector.The temporal generator is denoted G0(z0, vl), and the image generator is conditioned similarly.
- 5.2. Conditional TGAN: The CTGAN discriminator broadcasts the one-hot label vector into a voxel, concatenates it with the input video, and processes the result with convolutional layers.
6. Experiments
Experiments evaluate TGAN across synthetic and practical video datasets, comparing its architecture and SVC training with 3D and Video GAN baselines. Results show sharper and more coherent generated videos, strong quantitative scores, semantic frame interpolation, and conditional generation, while revealing dataset and model-specific limitations.
- Datasets: Experiments use Moving MNIST, UCF-101, and the golf scene dataset to evaluate generated-video quality and model behavior.Moving MNIST contains 16-frame clips of two moving digits; UCF-101 and golf videos provide practical video settings, with golf backgrounds stabilized.
- Qualitative evaluation: On Moving MNIST, TGAN produces sharper, clearer frames with more discriminable digits than prior video-prediction results.The generated digits continue moving in straight lines, although their shapes can slightly change over time.
- Qualitative evaluation: Removing z0 from TGAN increases mode collapse, while the 3D model with normal GAN training produces the lowest-quality videos.The 3D model trained with SVC improves over normal GAN training but remains below the full TGAN configuration.
- Qualitative evaluation: On UCF-101, the 3D model has the most artifacts, whereas Video GAN reduces artifacts but more often generates unidentified videos than TGAN.The authors attribute this difference mainly to Video GAN's vulnerability to videos with moving backgrounds.
- Qualitative evaluation: On the stabilized golf scene dataset, TGAN video quality is reported as almost the same as Video GAN despite conditions favoring Video GAN's fixed-background assumption.The dataset contains fixed backgrounds created through stabilization, which is advantageous for the comparison baseline.
- Applications: TGAN interpolates semantically between adjacent latent variables and extends to conditional generation, whose video quality is reported as significantly higher than unsupervised TGAN.The conditional model uses category information, while frame interpolation generates intermediate frames rather than dissolve-like blends.
- Quantitative evaluation: Table 3 reports that TGAN generates samples that better deceive other methods, while Table 4 reports the best inception scores among the compared models.SVC slightly improves TGAN's inception score over conventional weight clipping and substantially stabilizes training, reducing hyperparameter-adjustment difficulty.
7. Summary
The paper proposes a video generative model that learns semantic representations and generates image sequences, while using Singular Value Clipping to stabilize WGAN training.
- TGAN learns semantic video representations and generates image sequences.
- The video-generation process is decomposed into latent-variable generation followed by image-sequence conversion.
- This representation produces better-quality videos and naturally supports frame interpolation.
- Singular Value Clipping stabilizes Wasserstein GAN training.