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November 21, 2024
The development of generative AI models for videos has made rapid progress in recent years. From the creation of short clips to longer, complex sequences, these technologies open up new possibilities in areas such as entertainment, education, and marketing. However, evaluating the quality and performance of such models is often difficult. Conventional metrics don't always provide an accurate picture of the actual quality of a generated video, and a differentiated analysis of the strengths and weaknesses of different models is essential for the further development of the technology.
To address these challenges, VBench++ was developed, a comprehensive benchmark suite for evaluating generative video AI models. VBench++ analyzes "video quality" based on specific, hierarchically structured, and independent dimensions, each equipped with tailored prompts and evaluation methods. The benchmark enables a detailed and objective assessment that considers both the technical aspects and the trustworthiness of the models.
VBench++ covers a wide range of evaluation dimensions, divided into the categories "Video Quality" and "Consistency with the Video Condition." "Video Quality" includes aspects such as:
"Consistency with the Video Condition" refers to the agreement of the generated video with the specifications of the input prompt, for example, concerning:
VBench++ is designed to evaluate a variety of tasks in the field of video generation, including text-to-video (T2V) and image-to-video (I2V). For I2V tasks, a special "Image Suite" with adaptive aspect ratio was developed to enable fair comparisons between different models. Furthermore, VBench++ also evaluates the trustworthiness of the models in terms of fairness, bias, and safety, to provide a holistic picture of model performance.
A crucial aspect of VBench++ is the validation of the results through human evaluation. For each evaluation dimension, human preference ratings were collected to ensure that the automatic evaluations align with human perception. This approach guarantees that the results of VBench++ are relevant and meaningful for the further development of generative video AI.
For Mindverse, as a provider of AI-powered content solutions, VBench++ offers a valuable resource for evaluating and optimizing its own video AI models. Through the detailed analysis of model performance, targeted improvements can be made and the quality of the generated videos continuously increased. Moreover, VBench++ contributes to transparency and comparability in the AI industry and promotes the development of innovative solutions in the field of video generation.
VBench++ represents an important step towards standardized and meaningful evaluation of generative video AI. The benchmark offers developers and users a comprehensive tool for assessing model quality and contributes to the further development of this promising technology. With the continuous expansion of the database with new models and evaluation dimensions, VBench++ will continue to play a central role in the landscape of generative video AI.
Bibliography:
Huang, Z., He, Y., Yu, J., Zhang, F., Si, C., Jiang, Y., … & Liu, Z. (2024). VBench: Comprehensive Benchmark Suite for Video Generative Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
Huang, Z., Zhang, F., Xu, X., He, Y., Yu, J., Dong, Z., … & Liu, Z. (2024). VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models. arXiv preprint arXiv:2411.13503.
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