Physics-IQ benchmark reveals that generative video models exhibit limited physical understanding unrelated to their visual quality.
Image quality assessment: from error visibility to structural similarity
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
verdicts
UNVERDICTED 4roles
dataset 1polarities
background 1representative citing papers
HRSino is a training-free adaptive diffusion inference approach for high-resolution sinogram completion that reduces peak memory by up to 30.81% and inference time by up to 17.58% while maintaining accuracy.
VFMTok builds a generalist image tokenizer on frozen VFMs using adaptive quantization and semantic alignment, delivering gFID 1.36 for autoregressive and 1.25 for continuous generation on ImageNet with 3x faster convergence.
The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.
citing papers explorer
-
Do generative video models understand physical principles?
Physics-IQ benchmark reveals that generative video models exhibit limited physical understanding unrelated to their visual quality.
-
Training-Free Inference for High-Resolution Sinogram Completion
HRSino is a training-free adaptive diffusion inference approach for high-resolution sinogram completion that reduces peak memory by up to 30.81% and inference time by up to 17.58% while maintaining accuracy.
-
Vision Foundation Models as Generalist Tokenizers for Image Generation
VFMTok builds a generalist image tokenizer on frozen VFMs using adaptive quantization and semantic alignment, delivering gFID 1.36 for autoregressive and 1.25 for continuous generation on ImageNet with 3x faster convergence.
-
World Action Models: The Next Frontier in Embodied AI
The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.