LikePhys: Evaluating Intuitive Physics Understanding in Video Diffusion
  Models via Likelihood Preference
Intuitive physics understanding in video diffusion models plays an essential
role in building general-purpose physically plausible world simulators, yet
accurately evaluating such capacity remains a challenging task due to the
difficulty in disentangling physics correctness from visual appearance in
generation. To the end, we introduce LikePhys, a training-free method that
evaluates intuitive physics in video diffusion models by distinguishing
physically valid and impossible videos using the denoising objective as an
ELBO-based likelihood surrogate on a curated dataset of valid-invalid pairs. By
testing on our constructed benchmark of twelve scenarios spanning over four
physics domains, we show that our evaluation metric, Plausibility Preference
Error (PPE), demonstrates strong alignment with human preference, outperforming
state-of-the-art evaluator baselines. We then systematically benchmark
intuitive physics understanding in current video diffusion models. Our study
further analyses how model design and inference settings affect intuitive
physics understanding and highlights domain-specific capacity variations across
physical laws. Empirical results show that, despite current models struggling
with complex and chaotic dynamics, there is a clear trend of improvement in
physics understanding as model capacity and inference settings scale.
68edc22cde1fee572713a89d
Jianhao Yuan
68edc22cde1fee572713a89e
Fabio Pizzati
68edc22cde1fee572713a89f
Francesco Pinto
68edc22cde1fee572713a8a0
Lars Kunze
68edc22cde1fee572713a8a1
Ivan Laptev
68edc22cde1fee572713a8a2
Paul Newman
68edc22cde1fee572713a8a3
Philip Torr
68edc22cde1fee572713a8a4
Daniele De Martini
LikePhys: Evaluating Intuitive Physics Understanding in Video Diffusion
  Models via Likelihood Preference
Intuitive physics understanding in video diffusion models plays an essential
role in building general-purpose physically plausible world simulators, yet
accurately evaluating such capacity remains a challenging task due to the
difficulty in disentangling physics correctness from visual appearance in
generation. To the end, we introduce LikePhys, a training-free method that
evaluates intuitive physics in video diffusion models by distinguishing
physically valid and impossible videos using the denoising objective as an
ELBO-based likelihood surrogate on a curated dataset of valid-invalid pairs. By
testing on our constructed benchmark of twelve scenarios spanning over four
physics domains, we show that our evaluation metric, Plausibility Preference
Error (PPE), demonstrates strong alignment with human preference, outperforming
state-of-the-art evaluator baselines. We then systematically benchmark
intuitive physics understanding in current video diffusion models. Our study
further analyses how model design and inference settings affect intuitive
physics understanding and highlights domain-specific capacity variations across
physical laws. Empirical results show that, despite current models struggling
with complex and chaotic dynamics, there is a clear trend of improvement in
physics understanding as model capacity and inference settings scale.
LikePhys evaluates intuitive physics in video diffusion models using a denoising objective-based metric, demonstrating better alignment with human preference than existing methods.
video diffusion models intuitive physics LikePhys denoising objective ELBO-based likelihood Plausibility Preference Error benchmark human preference model design inference settings domain-specific capacity variations