arXiv (All)
open access
Why Do Video Diffusion Models Violate Physics? Unveiling the Flaws in Attention Mechanisms
Despite impressive visual quality, state-of-the-art video diffusion models often generate content that violates real-world physical laws. While existing solutions rely on external priors or specialized data, we investigate the root cause by exploring the internal mechanisms of these models. Specific
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Physically Based Rendering in the Latent Space#1035024
- Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection#1070663
- PhysPlan: Grounded Physical State Reasoning and Graph-Guided Optimization for Physically Plausible Video Generation#1131656
- DraftAttention2: Fast Video Diffusion with Low-Resolution-Guided Mixed-Precision Attention#1116959
- Training-free image inversion for one-step diffusion models#1129283
Record · ID 1049395
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive