Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation (1811.09393v4)

Abstract: Our work explores temporal self-supervision for GAN-based video generation tasks. While adversarial training successfully yields generative models for a variety of areas, temporal relationships in the generated data are much less explored. Natural temporal changes are crucial for sequential generation tasks, e.g. video super-resolution and unpaired video translation. For the former, state-of-the-art methods often favor simpler norm losses such as $L^2$ over adversarial training. However, their averaging nature easily leads to temporally smooth results with an undesirable lack of spatial detail. For unpaired video translation, existing approaches modify the generator networks to form spatio-temporal cycle consistencies. In contrast, we focus on improving learning objectives and propose a temporally self-supervised algorithm. For both tasks, we show that temporal adversarial learning is key to achieving temporally coherent solutions without sacrificing spatial detail. We also propose a novel Ping-Pong loss to improve the long-term temporal consistency. It effectively prevents recurrent networks from accumulating artifacts temporally without depressing detailed features. Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution. A series of user studies confirm the rankings computed with these metrics. Code, data, models, and results are provided at https://github.com/thunil/TecoGAN. The project page https://ge.in.tum.de/publications/2019-tecogan-chu/ contains supplemental materials.

• The paper introduces a self-supervised adversarial framework that enhances both spatial and temporal coherence in video generation.
• It proposes the novel Ping-Pong loss to prevent artifact accumulation in recurrent networks, ensuring long-term consistency.
• The study establishes new metrics validated by user studies, outperforming state-of-the-art methods in VSR and UVT tasks.

Overview of "Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation"

This paper investigates the challenge of generating temporally coherent video sequences using GANs, highlighting the limitations of existing methods in capturing natural temporal changes. The authors propose a novel temporally self-supervised adversarial learning framework, particularly focusing on improving tasks such as video super-resolution (VSR) and unpaired video translation (UVT).

Key Contributions

1. Temporally Self-Supervised Learning: The paper introduces a novel algorithm that enhances both spatial and temporal coherence in video generation without sacrificing spatial detail. Unlike traditional methods that rely heavily on spatial discriminators, this approach utilizes a spatio-temporal discriminator for more robust video generation.
2. Ping-Pong Loss: A novel "Ping-Pong" loss is proposed to prevent recurrent networks from accumulating artifacts over time, a common issue in video generator networks. This loss is crucial for maintaining long-term temporal consistency without suppressing spatial details.
3. New Metrics for Evaluation: The authors develop the first set of metrics to quantitatively evaluate temporal accuracy and perceptual quality in video evolution. The validity of these metrics is substantiated through user studies.

Numerical Results

• The paper presents compelling numerical evidence demonstrating the superiority of their approach in achieving temporal coherence. They report improved perceptual quality using proposed metrics, outperforming state-of-the-art methods such as FRVSR, DUF, CycleGAN, and RecycleGAN in both VSR and UVT tasks.

Implications and Future Directions

The proposed framework and its associated techniques have several implications for the field of AI and video generation:

• Practical Impact: This work provides a viable solution for creating temporally coherent videos, which is crucial for applications in film production, video editing, and real-time graphics.
• Theoretical Insights: The introduction of the Ping-Pong loss and spatio-temporal discriminators opens new avenues in the theoretical exploration of adversarial networks, particularly in understanding temporal dynamics.
• Future Research: The paper sets a foundation for further research into reducing computational overhead while retaining temporal coherence, optimizing the trade-offs between spatial detail and temporal accuracy, and extending methodologies to other sequential data processing tasks.

Conclusion

The paper presents significant advancements in GAN-based video generation, primarily by focusing on the interplay between spatial quality and temporal consistency. This approach marks a step forward in overcoming the temporal challenges present in existing GAN models, setting a new benchmark for future research in video synthesis and related applications.