DynamicsBoost: Dynamic Plausible Video Generation via Annotation-Free Continuation Preference Optimization

Despite significant progress in text-to-video generation, current models still suffer from unrealistic dynamics, temporal inconsistency, and unstable semantic alignment. Existing preference alignment approaches rely on costly and often ambiguous human or VLM-based video preference annotation, which has become a major bottleneck for scaling data. To address this challenge, we propose an annotation-free preference alignment method that constructs accurate preference pairs through video continuation.We extend a pretrained video generation model into a continuation model and apply continuation with different amounts of reference frames while keeping the total video length fixed. As generated segments are inferior to ground-truth frames and and fixed-length continuations conditioned on more reference frames contain less generated content, they exhibit higher fidelity than those with fewer references, naturally inducing a preference order.We further introduce Asymmetrical DPO, which computes preference loss on all continuation regions except the shared prefix conditioning frames and normalizes it by their length, preventing spurious preference signals from leaking into the conditioned portion.Experiments across multiple benchmarks show that our method delivers significant improvements in dynamics realism, temporal coherence, and semantic alignment over existing DPO-based approaches, while fully eliminating the need for human preference labeling or auxiliary reward models.