Neuro-Symbolic Evaluation of Text-to-Video Models using Formalf Verification

• URL: http://arxiv.org/abs/2411.16718v1
• Date: Fri, 22 Nov 2024 23:59:12 GMT
• Title: Neuro-Symbolic Evaluation of Text-to-Video Models using Formalf Verification
• Authors: S. P. Sharan, Minkyu Choi, Sahil Shah, Harsh Goel, Mohammad Omama, Sandeep Chinchali,
• Abstract summary: We introduce NeuS-V, a novel synthetic video evaluation metric. NeuS-V rigorously assesses text-to-video alignment using neuro-symbolic formal verification techniques. We find that NeuS-V demonstrates a higher correlation by over 5x with human evaluations when compared to existing metrics.
• Score: 5.468979600421325
• License:
• Abstract: Recent advancements in text-to-video models such as Sora, Gen-3, MovieGen, and CogVideoX are pushing the boundaries of synthetic video generation, with adoption seen in fields like robotics, autonomous driving, and entertainment. As these models become prevalent, various metrics and benchmarks have emerged to evaluate the quality of the generated videos. However, these metrics emphasize visual quality and smoothness, neglecting temporal fidelity and text-to-video alignment, which are crucial for safety-critical applications. To address this gap, we introduce NeuS-V, a novel synthetic video evaluation metric that rigorously assesses text-to-video alignment using neuro-symbolic formal verification techniques. Our approach first converts the prompt into a formally defined Temporal Logic (TL) specification and translates the generated video into an automaton representation. Then, it evaluates the text-to-video alignment by formally checking the video automaton against the TL specification. Furthermore, we present a dataset of temporally extended prompts to evaluate state-of-the-art video generation models against our benchmark. We find that NeuS-V demonstrates a higher correlation by over 5x with human evaluations when compared to existing metrics. Our evaluation further reveals that current video generation models perform poorly on these temporally complex prompts, highlighting the need for future work in improving text-to-video generation capabilities.

Related papers

• xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations [120.52120919834988]
  xGen-SynVideo-1 is a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. VidVAE compresses video data both spatially and temporally, significantly reducing the length of visual tokens. DiT model incorporates spatial and temporal self-attention layers, enabling robust generalization across different timeframes and aspect ratios.
  arXiv  Detail & Related papers  (2024-08-22T17:55:22Z)
• TC-Bench: Benchmarking Temporal Compositionality in Text-to-Video and Image-to-Video Generation [97.96178992465511]
  We argue that generated videos should incorporate the emergence of new concepts and their relation transitions like in real-world videos as time progresses. To assess the Temporal Compositionality of video generation models, we propose TC-Bench, a benchmark of meticulously crafted text prompts, corresponding ground truth videos, and robust evaluation metrics.
  arXiv  Detail & Related papers  (2024-06-12T21:41:32Z)
• Towards A Better Metric for Text-to-Video Generation [102.16250512265995]
  Generative models have demonstrated remarkable capability in synthesizing high-quality text, images, and videos. We introduce a novel evaluation pipeline, the Text-to-Video Score (T2VScore) This metric integrates two pivotal criteria: (1) Text-Video Alignment, which scrutinizes the fidelity of the video in representing the given text description, and (2) Video Quality, which evaluates the video's overall production caliber with a mixture of experts.
  arXiv  Detail & Related papers  (2024-01-15T15:42:39Z)
• EvalCrafter: Benchmarking and Evaluating Large Video Generation Models [70.19437817951673]
  We argue that it is hard to judge the large conditional generative models from the simple metrics since these models are often trained on very large datasets with multi-aspect abilities. Our approach involves generating a diverse and comprehensive list of 700 prompts for text-to-video generation. Then, we evaluate the state-of-the-art video generative models on our carefully designed benchmark, in terms of visual qualities, content qualities, motion qualities, and text-video alignment with 17 well-selected objective metrics.
  arXiv  Detail & Related papers  (2023-10-17T17:50:46Z)
• VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation [73.54366331493007]
  VideoGen is a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency. We leverage an off-the-shelf text-to-image generation model, e.g., Stable Diffusion, to generate an image with high content quality from the text prompt.
  arXiv  Detail & Related papers  (2023-09-01T11:14:43Z)
• Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning [50.60891619269651]
  Control-A-Video is a controllable T2V diffusion model that can generate videos conditioned on text prompts and reference control maps like edge and depth maps. We propose novel strategies to incorporate content prior and motion prior into the diffusion-based generation process. Our framework generates higher-quality, more consistent videos compared to existing state-of-the-art methods in controllable text-to-video generation.
  arXiv  Detail & Related papers  (2023-05-23T09:03:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.