TRAVL: A Recipe for Making Video-Language Models Better Judges of
  Physics Implausibility
Despite impressive visual fidelity, modern video generative models frequently
produce sequences that violate intuitive physical laws, such as objects
floating, teleporting, or morphing in ways that defy causality. While humans
can easily detect such implausibilities, there remains no robust method for
quantitatively assessing physical realism in video. In this work, we explore
whether Video-Language Models (VLMs) can be trained to serve as reliable judges
of physical plausibility. We find that existing VLMs struggle to identify
physics violations, exposing fundamental limitations in their temporal and
causal reasoning. To address this, we introduce TRAVL, a fine-tuning recipe
that combines a balanced training dataset with a trajectory-aware attention
module to improve motion encoding and discrimination in VLMs. To evaluate
physical reasoning more rigorously, we propose ImplausiBench, a benchmark of
300 videos (150 real, 150 generated) that removes linguistic biases and
isolates visual-temporal understanding. Performance is reported both with
gold-standard human judgments and stricter LLM-as-judge metrics. Together,
TRAVL and ImplausiBench offer a unified framework for probing and improving
physical plausibility in multimodal models, shedding light on a challenging and
underexplored aspect of visual-temporal understanding.
68e854aa95e8e6771df38857
Saman Motamed
68e854aa95e8e6771df38858
Minghao Chen
68e854aa95e8e6771df38859
Luc Van Gool
68e854aa95e8e6771df3885a
Iro Laina
TRAVL: A Recipe for Making Video-Language Models Better Judges of
  Physics Implausibility
Despite impressive visual fidelity, modern video generative models frequently
produce sequences that violate intuitive physical laws, such as objects
floating, teleporting, or morphing in ways that defy causality. While humans
can easily detect such implausibilities, there remains no robust method for
quantitatively assessing physical realism in video. In this work, we explore
whether Video-Language Models (VLMs) can be trained to serve as reliable judges
of physical plausibility. We find that existing VLMs struggle to identify
physics violations, exposing fundamental limitations in their temporal and
causal reasoning. To address this, we introduce TRAVL, a fine-tuning recipe
that combines a balanced training dataset with a trajectory-aware attention
module to improve motion encoding and discrimination in VLMs. To evaluate
physical reasoning more rigorously, we propose ImplausiBench, a benchmark of
300 videos (150 real, 150 generated) that removes linguistic biases and
isolates visual-temporal understanding. Performance is reported both with
gold-standard human judgments and stricter LLM-as-judge metrics. Together,
TRAVL and ImplausiBench offer a unified framework for probing and improving
physical plausibility in multimodal models, shedding light on a challenging and
underexplored aspect of visual-temporal understanding.
TRAVL, a fine-tuning recipe with a trajectory-aware attention module, improves physical plausibility in Video-Language Models using the ImplausiBench benchmark.
Video-Language Models VLMs TRAVL trajectory-aware attention motion encoding discrimination ImplausiBench physical plausibility visual-temporal understanding LLM-as-judge