REVIEW 6 cited by
TC-Bench: Benchmarking Temporal Compositionality in Text-to-Video and Image-to-Video Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Video generation has many unique challenges beyond those of image generation. The temporal dimension introduces extensive possible variations across frames, over which consistency and continuity may be violated. In this study, we move beyond evaluating simple actions and 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. The prompts articulate the initial and final states of scenes, effectively reducing ambiguities for frame development and simplifying the assessment of transition completion. In addition, by collecting aligned real-world videos corresponding to the prompts, we expand TC-Bench's applicability from text-conditional models to image-conditional ones that can perform generative frame interpolation. We also develop new metrics to measure the completeness of component transitions in generated videos, which demonstrate significantly higher correlations with human judgments than existing metrics. Our comprehensive experimental results reveal that most video generators achieve less than 20% of the compositional changes, highlighting enormous space for future improvement. Our analysis indicates that current video generation models struggle to interpret descriptions of compositional changes and synthesize various components across different time steps.
Forward citations
Cited by 6 Pith papers
-
VGIF-Score: Interpretable and Diagnostic Evaluation of Spatio-Temporal Instruction Following in Video Generation
VGIF-Score decomposes video prompts into dependency graphs and uses a VLM to diagnose which instruction constraints models satisfy, revealing strong failures on causal and late-prompt constraints.
-
ARGUS: Hallucination and Omission Evaluation in Video-LLMs
ARGUS measures hallucination and omission in free-form video captions using LLM-based entailment and temporal alignment, finding that even the best video-LLM still produces roughly 40% hallucinated content.
-
NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding
NarrativeTrack shows that video AI models, including GPT-4o, falter at tracking a specific person across scene changes, outfit changes, and similar-looking characters in long videos.
-
A Survey of Automatic Evaluation Methods on Text, Visual and Speech Generations
A unified taxonomy and comparative meta-evaluation of automatic evaluation methods across text, vision, and speech generation, concluding that LLM-based evaluators dominate current practice.
-
From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence
Physical intelligence needs an embodied brain that reasons over interventions and emits capability requests, grounded by a physical harness and shared experience contracts rather than direct actuator policies.
-
T2VWorldBench: A Benchmark for Evaluating World Knowledge in Text-to-Video Generation
A 1,200-prompt benchmark across six world-knowledge domains reports that ten state-of-the-art text-to-video models average below 0.70 on a 0 to 1 scale for producing videos consistent with real-world knowledge.
Discussion (0). Sign in to comment.