Source-linked AI summary
Recycle-GAN: Unsupervised Video Retargeting
Aayush Bansal, Shugao Ma, Deva Ramanan, Yaser Sheikh
TL;DR
Unsupervised video retargeting lacks paired correspondences while needing to preserve target-domain style and sequential content. Recycle-GAN combines spatial and temporal constraints with adversarial learning, demonstrating retargeting across faces, flowers, and natural processes. The paper argues that temporal information improves mappings over spatial-only objectives, while also documenting a failure beyond the training data distribution.
Problem
Unpaired video and image translation lacks correspondences between input and output samples, while spatial-only objectives can suffer perceptual mode collapse and input-tied reconstructions.
Method
Recycle-GAN combines spatiotemporal cues with conditional generative adversarial networks and uses temporal prediction to define a recycle loss across domains and time.
Results
The approach qualitatively demonstrates video retargeting for faces, flowers, clouds, winds, sunrise, and sunset, and human subjects chose its synthesized videos as more natural 76% of the time versus 8% for Cycle-GAN.
Takeaways & Limitations
Temporal information provides additional optimization constraints and, combined with spatial information, helps learn target-domain style without manual supervision.
Takeaways & Limitations
The approach can fail beyond the training data distribution, as shown when an origami bird reappears during flight because training data lacked frames without the origami bird.
Abstract
from arXiv · showhide
We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver's speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert's style. Our approach combines both spatial and temporal information along with adversarial losses for content translation and style preservation. In this work, we first study the advantages of using spatiotemporal constraints over spatial constraints for effective retargeting. We then demonstrate the proposed approach for the problems where information in both space and time matters such as face-to-face translation, flower-to-flower, wind and cloud synthesis, sunrise and sunset.
1 Introduction
Recycle-GAN targets unsupervised video retargeting by transferring sequential content across domains while preserving target-domain style. It uses spatiotemporal constraints with adversarial learning, motivated by the limits of spatial-only translation, and demonstrates the approach across faces, flowers, and natural processes.
- Recycle-GAN introduces unsupervised, data-driven video retargeting that transfers sequential content between domains.
- Spatial-only translation can produce bad local minima or perceptual mode collapse and struggles to learn domain style because stylistic information requires temporal knowledge.
- Temporal information adds optimization constraints, while combined spatial and temporal constraints help learn identity-specific style without manual supervision.
- The approach incorporates spatiotemporal cues into conditional generative adversarial networks for video retargeting.
- Qualitative demonstrations cover face-to-face translation, flower-to-flower translation, cloud and wind synthesis, and sunrise and sunset alignment.
2 Related Work
Prior image-to-image translation methods commonly require paired supervision or use spatial cycle consistency for unpaired data. The paper argues that spatial-only objectives can fail through perceptual collapse and input-tied reconstructions, motivating spatiotemporal constraints for unpaired translation.
- Paired image-to-image translation methods require input-output pairs, which are difficult to create for many tasks.
- Cycle-consistency enables unpaired image translation without expensive manual labeling, but Cycle-GAN variants consider spatial information in 2D images while ignoring temporal information.
- Spatial cycle consistency can yield perceptually similar outputs for different inputs while preserving reconstructions through hidden encodings.
- Reconstruction loss can force solutions to remain closely tied to the input, which is less suitable when temporal and stylistic information matters.
- The paper explores spatiotemporal constraints for video retargeting and unpaired image-to-image translation, where natural visual signals provide temporal constraints without added annotation.
3 Method
Recycle-GAN learns unsupervised mappings between video domains by combining adversarial, cycle, recurrent, and recycle losses. It exploits ordered streams and temporal prediction during training, while inference can generate frames directly or smooth them temporally.
- Adversarial and cycle losses: The approach uses adversarial training to distinguish real target-domain samples from generated samples GY(x), without requiring paired data.Training only needs individual samples from the two domains rather than manually paired examples.
- Adversarial and cycle losses: Cycle consistency reconstructs samples across the two learned mappings when paired examples are unavailable.This provides the standard cycle-based reconstruction component of the method.
- Temporal constraints: For ordered but unpaired streams, a recurrent predictor PX learns future samples from past frames, allowing the method to use temporal ordering.The method treats video streams as sequences rather than shuffled collections of independent frames.
- Temporal constraints: Recycle loss imposes a cycle across domains and time, requiring translated frame sequences to map back to themselves.The translated sequence GY(x1:t) is incorporated into the temporal reconstruction constraint.
- Final formulation: The final objective combines adversarial, recycle, and recurrent losses in both domain directions, weighted by λrx, λry, λτx, and λτy.This formulation jointly trains spatial translation and temporal prediction components.
- Inference and implementation: At inference, the trained generator can translate each frame independently, while the temporal predictor offers an alternative for smoothing output videos.The implementation uses single-frame generation for applicability to both videos and single images and for comparison with spatial methods.
- Inference and implementation: Experiments use 256 × 256 images, a 70 × 70 PatchGAN discriminator, λs = 10, and temporal predictors receiving the last two frames.The temporal predictors use U-Net-like architectures.
4 Experiments
Experiments compare Recycle-GAN with spatial Cycle-GAN using quantitative segmentation and image-synthesis evaluations, qualitative translations, and human judgments. Across these settings, spatiotemporal constraints improve retargeting, while performance remains limited by the coverage of the training distribution.
- 4.1 Quantitative Analysis: The Viper evaluation compares spatial and spatiotemporal approaches on held-out video sequences with known correspondence used only for evaluation.The study uses 57 sequences for training and 20 held-out sequences, spanning diverse environmental conditions.
- 4.1 Quantitative Analysis: Recycle-GAN outperforms Cycle-GAN on image-to-labels segmentation, and combining spatial and temporal losses further improves performance.The comparison uses Mean Pixel Accuracy, Average Class Accuracy, and Intersection over Union.
- 4.1 Quantitative Analysis: Cycle-GAN initially learns a good translation but degrades as reconstruction loss decreases, whereas additional constraints lead to a better local minimum.The authors attribute the degradation to optimizing reconstruction loss alone and note that varied illumination and weather make Viper more challenging than Cityscapes.
- 4.2 Qualitative Analysis: Qualitative experiments demonstrate unsupervised face-to-face translation that captures identity-specific expressions, including smiles, dimples, mouth shapes, and facial lines.Examples include John Oliver–Stephen Colbert, Barack Obama–Donald Trump, MLK–Barack Obama, and John Oliver–a cartoon character.
- 4.2 Qualitative Analysis: The method also aligns flower events and synthesizes cloud and wind conditions while preserving the scene’s aesthetics.Flower experiments align blooming or dying events, while weather experiments translate calm and windy conditions without modifying the place’s aesthetics.
- 4.4 Human Studies: In human studies, subjects judged Recycle-GAN outputs more natural than Cycle-GAN outputs, selecting them 76% of the time in direct comparison and 74.7% during translation comparison.Generated videos from Recycle-GAN were also misclassified as real 28.3% of the time, versus 7.3% for Cycle-GAN.
- 4.5 Failure Example: Learning association beyond data distribution: A bird-to-origami-bird translation fails when the target training data lacks examples corresponding to the real bird becoming invisible.The authors suggest the approach interpolates within the given data distribution and may require substantially more training data for broader scenario coverage.
5 Discussion & Future Work
The paper frames unsupervised video retargeting and image translation as under-constrained, while showing that naturally available temporal signals can strengthen learned mappings. Future work could use spatiotemporal models to represent timing variation in style more precisely.
- Unpaired and unsupervised video retargeting and image translation lack correspondences between input and output samples, making them under-constrained.
- Natural visual signals provide strong temporal constraints for free, resulting in significantly better mappings.
- Recycle-GANs learn both a mapping function and a recurrent temporal predictor, but reported results use only the mapping function for fair comparison.
- Spatiotemporal generative models could represent style more precisely by learning the speed and timing variation of generated content.