Risk Decomposition for Hallucination-Aware Video Sampling
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2601.07823v2 Announce Type: cross Abstract: Video world models have emerged as promising candidates for high-fidelity world models, offering the potential to synthesize high-quality videos capt...
PhysPlan is a training‑free guidance framework that enhances video diffusion models by incorporating physical awareness through agentic physics simulation. It uses a vision‑language model to generate a Chain‑of‑Visual‑Thought representation of kinematic trajectories and 3D depth, which then drives an object‑centric test‑time optimization that isolates kinematic changes and locks the passive environment. The framework also employs Kinetic Intensity Profiling to adapt hyperparameters to varying physical deformations, and demonstrates superior performance on PhyGenBench and Physics‑IQ benchmarks compared to existing VDM baselines.
Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the ground-truth dynamics. We hypothesize that hallucination concentrates in low-coverage regions of the state-action space, where lightweight data-centric signals can both detect it and guide mitigation.
arXiv:2606. 27326v1 Announce Type: new Abstract: Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the ground-truth dynamics.
MotionHalluc is a benchmark designed to evaluate motion hallucinations in cross‑video comparison tasks, consisting of 1,540 fine‑grained questions over 553 video pairs. It assesses hallucinations across three dimensions—directional, attributional, and temporal—and reveals that current large multimodal models are highly susceptible to these errors. The authors also introduce a training‑free Perceive‑Parse‑Verify baseline that injects explicit kinematic measurements, achieving an average 10.6% performance improvement across models.
arXiv:2510. 08073v2 Announce Type: replace-cross Abstract: AI-generated videos have achieved near-perfect visual realism (e.