VGI-Bench: Probing Visual Intelligence in Video Generation Models

• URL: http://arxiv.org/abs/2608.19583v3
• Date: Wed, 26 Aug 2026 01:14:41 GMT
• Title: VGI-Bench: Probing Visual Intelligence in Video Generation Models
• Abstract summary: VGI-bench is a fine-grained evaluation of visual reasoning capabilities of video generation models.<n>Our evaluations show that current generative systems can solve a subset of visually grounded reasoning tasks, but remain far from reliable.<n>We hope VGI-bench will help stimulate the development of next-generation video generation models.
• Score: 69.37282368476755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies suggest that video generation models can exhibit certain forms of zero-shot visual reasoning through generated frames. Yet reliable evaluation remains challenging: benchmarks should adopt inputs aligned with the visual priors of current video models, require valid evolving processes rather than only plausible final states, and calibrate task difficulty to remain challenging yet partly feasible. To this end, we introduce VGI-bench, containing 27 tasks and 810 instances, organized by a two-level taxonomy of task domains and skill tags for fine-grained evaluation of visual reasoning capabilities of video generation models. Our evaluations show that current generative systems can solve a subset of visually grounded reasoning tasks, but remain far from reliable, with even the strongest model, Seedance 2.0, achieving only 51.0% under our evaluation criteria. Our analysis further explore the output failure modes, input condition sensitivity, performance transfer boundary from synthetic fine-tuning, and internal denoising perspective revealing limited self-correction, where later steps mainly refine early hypotheses rather than correct reasoning errors. We hope VGI-bench will help stimulate the development of next-generation video generation models. Website: https://hexuan21.github.io/VGI-Bench/

Related papers

• Not Another Text Benchmark: Putting the "Visual" Back in Visual Question Answering for Large Video Models [97.0559653915626]
  We introduce three new vision-centric evaluation benchmarks in temporal frame retrieval, video future prediction, and causal memory distortion.<n>We show that current frontier models exhibit significant weakness when attempting to reason through visual queries, rather than text.
  arXiv  Detail & Related papers  (2026-09-15T12:41:08Z)
• Training-free Detection of Generated Videos via Spatial-Temporal Likelihoods [6.921364920053057]
  We introduce STALL, a training-free, theoretically justified detector that provides likelihood-based scoring for videos.<n>We evaluate STALL on two public benchmarks and introduce ComGenVid, a new benchmark with state-of-the-art generative models.
  arXiv  Detail & Related papers  (2026-03-16T09:26:56Z)
• CoF-T2I: Video Models as Pure Visual Reasoners for Text-to-Image Generation [52.0601996237501]
  Chain-of-Frame (CoF) reasoning enables frame-by-frame visual inference.<n>CoF-T2I integrates CoF reasoning into text-to-image (T2I) generation via progressive visual refinement.<n>Experiments show that CoF-T2I significantly outperforms the base video model.
  arXiv  Detail & Related papers  (2026-01-15T04:33:06Z)
• VIPER: Process-aware Evaluation for Generative Video Reasoning [64.86465792516658]
  We introduce VIPER, a comprehensive benchmark spanning 16 tasks across temporal, structural, symbolic, spatial, physics, and planning reasoning.<n>Our experiments reveal that state-of-the-art video models achieve only about 20% POC@1.0 and exhibit a significant outcome-hacking.
  arXiv  Detail & Related papers  (2025-12-31T16:31:59Z)
• RULER-Bench: Probing Rule-based Reasoning Abilities of Next-level Video Generation Models for Vision Foundation Intelligence [24.51106324851909]
  We introduce RULER-Bench, a benchmark designed to evaluate the reasoning ability of video generation models from the perspective of cognitive rules.<n>For the evaluation of each generated video, we construct a checklist covering four metrics and leverage GPT-o3 to assign scores to each question.<n>Experiments show that the state-of-the-art model achieves only 48.87% on the rule coherence metric.
  arXiv  Detail & Related papers  (2025-12-02T10:29:51Z)
• TiViBench: Benchmarking Think-in-Video Reasoning for Video Generative Models [42.763907973320464]
  TiViBench is a hierarchical benchmark designed to evaluate the reasoning capabilities of image-to-video (I2V) generation models.<n>We introduce VideoTPO, a simple yet effective test-time strategy inspired by preference optimization.<n>Together, TiViBench and VideoTPO pave the way for evaluating and advancing reasoning in video generation models.
  arXiv  Detail & Related papers  (2025-11-17T18:52:44Z)
• Envisioning Beyond the Pixels: Benchmarking Reasoning-Informed Visual Editing [84.16442052968615]
  We introduce RISEBench, the first benchmark for evaluating Reasoning-Informed viSual Editing (RISE)<n>RISEBench focuses on four key reasoning categories: Temporal, Causal, Spatial, and Logical Reasoning.<n>We conduct experiments evaluating nine prominent visual editing models, comprising both open-source and proprietary models.
  arXiv  Detail & Related papers  (2025-04-03T17:59:56Z)
• ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models [55.07988373824348]
  We study the visual generalization capabilities of three existing robotic foundation models.<n>Our study shows that the existing models do not exhibit robustness to visual out-of-domain scenarios.<n>We propose a gradual backbone reversal approach founded on model merging.
  arXiv  Detail & Related papers  (2024-09-23T17:47:59Z)
• FitVid: Overfitting in Pixel-Level Video Prediction [117.59339756506142]
  We introduce a new architecture, named FitVid, which is capable of severe overfitting on the common benchmarks. FitVid outperforms the current state-of-the-art models across four different video prediction benchmarks on four different metrics.
  arXiv  Detail & Related papers  (2021-06-24T17:20:21Z)
• Transformation-based Adversarial Video Prediction on Large-Scale Data [19.281817081571408]
  We focus on the task of video prediction, where given a sequence of frames extracted from a video, the goal is to generate a plausible future sequence. We first improve the state of the art by performing a systematic empirical study of discriminator decompositions. We then propose a novel recurrent unit which transforms its past hidden state according to predicted motion-like features.
  arXiv  Detail & Related papers  (2020-03-09T10:52:25Z)

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.