AI 影片生成，是最近最熱門的領域之一。各大學實驗室、網路巨頭 AI Lab、新創公司紛紛加入了 AI 影片生成的賽道。 Pika、Gen-2、Show-1、VideoCrafter、ModelScope、SEINE、LaVie、VideoLDM 等影片產生模型的發布，更是讓人眼睛一亮。 v⁽ⁱ⁾
大家一定對以下幾個問題感到好奇：
為此，我們推出了VBench，一個全面的「視訊生成模型的評測框架」，旨在向用戶提供關於各種視訊模型的優劣和特點。透過VBench，使用者可以了解不同視訊模型的強項和優勢。
#VBench不僅能全面、細緻地評估影片生成效果，也能提供符合人們感官體驗的評估，節省時間和精力。
已開源的AI視訊生成模型
各個開源的AI 視訊產生模型在 VBench 上的表現如下。
在以上 6 個模型中，可以看到 VideoCrafter-1.0 和 Show-1 在大多數維度都有相對優勢。
新創公司的影片產生模型
#VBench 目前給了Gen-2 和Pika 這兩家創業公司模式的評測結果。
Gen-2 和 Pika 在 VBench 上的表現。在雷達圖中，為了更清晰地視覺化比較，我們加入了 VideoCrafter-1.0 和 Show-1 作為參考，同時將每個維度的評測結果歸一化到了 0.3 與 0.8 之間。
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Performance of Gen-2 and Pika on VBench. We include the numerical results of VideoCrafter-1.0 and Show-1 as reference.
It can be seen that Gen-2 and Pika have obvious advantages in video quality (Video Quality), such as timing consistency (Temporal Consistency) and single frame quality (Aesthetic Quality and Imaging Quality) related dimensions. In terms of semantic consistency with user input prompts (such as Human Action and Appearance Style), partial-dimensional open source models will be better.
Video generation model VS picture generation model
Video generation model VS Image generation model. Among them, SD1.4, SD2.1 and SDXL are image generation models.
The performance of the video generation model in 8 major scene categories
The following are the performance of different models in 8 different categories evaluation results on.
At present, VBench is fully open source. And supports one-click installation. Everyone is welcome to play, test the models you are interested in, and work together to promote the development of the video generation community.
#Open source address ：https://github.com/Vchitect/VBench
We have also open sourced a series of Prompt Lists : https://github.com/Vchitect/VBench/tree/master/prompts, including Benchmarks for evaluation in different capability dimensions, as well as evaluation Benchmarks on different scenario content.
The word cloud on the left shows the distribution of high-frequency words in our Prompt Suites, and the picture on the right shows the statistics of the number of prompts in different dimensions and categories.
For each dimension, we calculated the correlation between the VBench evaluation results and the manual evaluation results to verify the consistency of our method with human perception. In the figure below, the horizontal axis represents the manual evaluation results in different dimensions, and the vertical axis shows the results of the automatic evaluation of the VBench method. It can be seen that our method is highly aligned with human perception in all dimensions.
VBench can not only evaluate existing models , More importantly, various problems that may exist in different models can also be discovered, providing valuable insights for the future development of AI video generation.
"Temporal continuity" and "video dynamic level": Don't choose one or the other, but improve both
We found that there is a certain trade-off relationship between temporal coherence (such as Subject Consistency, Background Consistency, Motion Smoothness) and the amplitude of motion in the video (Dynamic Degree). For example, Show-1 and VideoCrafter-1.0 performed very well in terms of background consistency and action smoothness, but scored lower in terms of dynamics; this may be because the generated "not moving" pictures are more likely to appear "in the timing" Very coherent." VideoCrafter-0.9, on the other hand, is weaker on the dimension related to timing consistency, but scores high on Dynamic Degree.
This shows that it is indeed difficult to achieve "temporal coherence" and "higher dynamic level" at the same time; in the future, we should not only focus on improving one aspect, but should also improve "temporal coherence" And "the dynamic level of the video", this is meaningful.
Evaluate by scene content to explore the potential of each model
Some models perform well in different categories There are big differences in performance. For example, in terms of aesthetic quality, CogVideo performs well in the "Food" category, but scores lower in the "LifeStyle" category. If the training data is adjusted, can the aesthetic quality of CogVideo in the "LifeStyle" categories be improved, thereby improving the overall video aesthetic quality of the model?
This also tells us that when evaluating video generation models, we need to consider the performance of the model under different categories or topics, explore the upper limit of the model in a certain capability dimension, and then target Improve the "holding back" scenario category.
Categories with complex motion: poor spatiotemporal performance
Categories with high spatial complexity, Scores in the aesthetic quality dimension are relatively low. For example, the "LifeStyle" category has relatively high requirements for the layout of complex elements in space, and the "Human" category poses challenges due to the generation of hinged structures.
For categories with complex timing, such as the "Human" category which usually involves complex movements and the "Vehicle" category which often moves faster, they score equally in all tested dimensions. relatively low. This shows that the current model still has certain deficiencies in processing temporal modeling. The temporal modeling limitations may lead to spatial blurring and distortion, resulting in unsatisfactory video quality in both time and space.
Difficult to generate categories: little benefit from increasing data volume
We use the commonly used video data set WebVid- 10M conducted statistics and found that about 26% of the data was related to "Human", accounting for the highest proportion among the eight categories we counted. However, in the evaluation results, the “Human” category was one of the worst performing among the eight categories.
This shows that for a complex category like "Human", simply increasing the amount of data may not bring significant improvements to performance. One potential method is to guide the learning of the model by introducing "Human" related prior knowledge or control, such as Skeletons, etc.
Millions of data sets: improving data quality takes precedence over data quantity
Although the "Food" category Occupying only 11% of WebVid-10M, it almost always has the highest aesthetic quality score in the review. So we further analyzed the aesthetic quality performance of different categories of content in the WebVid-10M data set and found that the "Food" category also had the highest aesthetic score in WebVid-10M.
This means that on the basis of millions of data, filtering/improving data quality is more helpful than increasing the amount of data.
Ability to be improved: Accurately generate multiple objects and the relationship between objects
Current video generation The model still cannot catch up with the image generation model (especially SDXL) in terms of "Multiple Objects" and "Spatial Relationship", which highlights the importance of improving combination capabilities. The so-called combination ability refers to whether the model can accurately display multiple objects in video generation, as well as the spatial and interactive relationships between them.
Potential solutions to this problem may include:
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