arXiv Computer Vision
MindWorldBench: Evaluating Mental-State-to-Behavior Reasoning in Image-to-Video Generation
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Hugging Face Trending Papers
4d ago
MindWorldBench: Evaluating Mental-State-to-Behavior Reasoning in Image-to-Video Generation
Current image-to-video models achieve visual realism and physical plausibility, but reasoning about mental states remains unexplored. Actions are driven by belief, desire, and perception, requiring in...
arXiv Computer Vision
Sep 11
From Evaluation to Enhancement: Benchmarking and Improving Think-with-Video Reasoning for Video Generative Models
The paper introduces VWG-Bench, a benchmark covering nine reasoning dimensions and 38 tasks to evaluate video generative models on symbolic reasoning, physical laws, and goal pursuit. It also presents Vid-PRE, a prompt-rewriting framework that offloads reasoning to a VLM, improving logical performance without changing the generator architecture. Experiments show that current models excel at visual quality but struggle with logic-heavy tasks, while Vid-PRE significantly boosts reasoning across multiple generators.
arXiv AI
Jul 21
Thinking in Video: Can Video Generators Really Reason About the Real World?
arXiv:2607. 17523v1 Announce Type: cross Abstract: Recent advances in world models and video generation have given rise to an emerging reasoning paradigm that leverages video generative models to simulate, predict, and reason about real-world dynamics.
arXiv AI
Aug 25
VGI-Bench: Probing Visual Intelligence in Video Generation Models
arXiv:2608.19583v2 Announce Type: replace-cross Abstract: Recent studies suggest that video generation models can exhibit certain forms of zero-shot visual reasoning through generated frames. Yet rel...
arXiv AI
Aug 21
VGI-BENCH: Probing Visual Intelligence in Video Generation Models
arXiv:2608. 19583v1 Announce Type: cross Abstract: Recent studies suggest that video generation models can exhibit certain forms of zero-shot visual reasoning through generated frames.
Hugging Face Trending Papers
Aug 20
VGI-BENCH: Probing Visual Intelligence in Video Generation Models
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.