# DuoMatching

DuoMatching introduces a unified joint-marginal distribution matching framework: beyond existing joint DMD, it applies direct frame-level marginal supervision from an image generator, uses LatentBridge to resolve the video-image latent mismatch, and uses Latent Variation Sampling to spread frame-level supervision across temporal segments; in causal and bidirectional few-step video generation it improves visual quality, composition and semantic alignment while largely preserving motion dynamics, with human overall preference above 80% against all evaluated baselines.

DuoMatching introduces a unified joint-marginal distribution matching framework: beyond existing joint DMD, it applies direct frame-level marginal supervision from an image generator, uses LatentBridge to resolve the video-image latent mismatch, and uses Latent Variation Sampling to spread frame-level supervision across temporal segments; in causal and bidirectional few-step video generation it improves visual quality, composition and semantic alignment while largely preserving motion dynamics, with human overall preference above 80% against all evaluated baselines.