Towards Accurate Generative Models of Video: A New Metric & Challenges (1812.01717v2)

Abstract: Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images. Following their successful application in image processing and representation learning, an important next step is to consider videos. Learning generative models of video is a much harder task, requiring a model to capture the temporal dynamics of a scene, in addition to the visual presentation of objects. While recent attempts at formulating generative models of video have had some success, current progress is hampered by (1) the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples, and (2) the wide gap between purely synthetic video data sets and challenging real-world data sets in terms of complexity. To this extent we propose Fr\'{e}chet Video Distance (FVD), a new metric for generative models of video, and StarCraft 2 Videos (SCV), a benchmark of game play from custom starcraft 2 scenarios that challenge the current capabilities of generative models of video. We contribute a large-scale human study, which confirms that FVD correlates well with qualitative human judgment of generated videos, and provide initial benchmark results on SCV.

Towards Accurate Generative Models of Video: A New Metric and Challenges

The paper presents a comprehensive paper aimed at improving generative models of video by addressing the limitations of current evaluation metrics and datasets. The research introduces the Fréchet Video Distance (FVD), a novel metric, and the StarCraft 2 Videos (SCV) dataset designed to test the capacities of video generative models under complex scenarios.

Key Contributions

1. Fréchet Video Distance (FVD): The authors propose FVD as a metric that builds on the Fréchet Inception Distance (FID), adapting it for video by incorporating temporal coherence alongside visual quality. Unlike frame-level metrics such as PSNR and SSIM, which rely on ground-truth sequences, FVD evaluates entire video distributions, making it applicable in generative adversarial setups.
2. StarCraft 2 Videos (SCV): SCV is a benchmark dataset constructed from StarCraft 2 gameplay. It challenges models with real-world complexity elements like relational reasoning and long-term memory. SCV scenarios include tasks like "Move Unit to Border" and "Collect Mineral Shards", emphasizing the importance of temporal dynamics and interactions in video data.
3. Empirical Validation and Human Studies: A large-scale human paper corroborates the effectiveness of FVD, demonstrating better alignment with human judgment compared to traditional metrics like SSIM and PSNR. This provides empirical evidence of FVD’s capability in assessing the qualitative aspects of generated videos.
4. Baseline Evaluation: The paper benchmarks modern video generation models, such as CDNA, SV2P, SVP-FP, and SAVP, showing variations in their performance on datasets like BAIR and KTH, as well as the newly introduced SCV benchmarks. The paper reveals ongoing challenges in modeling complex temporal dynamics and consistency.

Strong Numerical Results and Bold Claims

• The introduction of FVD represents a significant advancement over existing metrics due to its ability to factor in both spatial and temporal aspects of video sequences. The paper asserts that FVD correlates more closely with human perceptions of video quality.
• The authors provide a robust claim on the usefulness of FVD by showing that differences as small as 50 FVD points correspond to perceptible differences in quality, urging the need for standardized sample sizes when comparing models.

Implications and Future Directions

The implications of this research are profound, particularly in pushing the boundaries of video generation techniques. The introduction of SCV as a benchmark suggests a new path forward in developing models that can effectively handle complex motion and interaction scenarios.

Theoretical developments resulting from FVD and SCV could lead to improved architectures for generative models, enhancing their ability to capture realistic temporal dynamics. Practically, these improvements can benefit applications ranging from autonomous vehicles to video synthesis in entertainment.

Future developments in AI could see extensions of this work where more nuanced and varied datasets are created, possibly incorporating real-time interaction and adaptation capabilities in video models. Furthermore, as generative models progress, new metrics may need to be developed to keep pace with the increasing complexity and quality of synthesized video data.

Overall, the paper tackles significant challenges in video generation and evaluation, providing researchers with tools and benchmarks that are crucial for advancing the field.