LaMo adds self-supervised latent motion priors via a motion drift loss during training and motion prior guidance during sampling to boost physical fidelity in video diffusion models like CogVideoX.
Physvideogen- erator: Towards physically aware video generation via latent physics guidance.arXiv preprint arXiv:2601.03665
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A fine-tuned video diffusion model becomes a fast, differentiable CFD surrogate for urban wind, enabling gradient-based building-layout optimization confirmed by ground-truth simulations.
citing papers explorer
-
LaMo: Self-Supervised Latent Motion Priors for Physical Realism in Video Generation
LaMo adds self-supervised latent motion priors via a motion drift loss during training and motion prior guidance during sampling to boost physical fidelity in video diffusion models like CogVideoX.
-
Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
A fine-tuned video diffusion model becomes a fast, differentiable CFD surrogate for urban wind, enabling gradient-based building-layout optimization confirmed by ground-truth simulations.