This paper presents OmniVTA, a world-model-based visuo-tactile manipulation framework designed for contact-rich robotic manipulation tasks such as wiping and assembly. The work introduces OmniViTac, a large-scale dataset comprising 21,000+ trajectories across 86 tasks and 100+ objects with six physics-grounded interaction patterns. The framework integrates four tightly coupled modules including a self-supervised tactile encoder and a two-stream visuo-tactile world model for predicting contact dynamics. The research addresses limitations in existing methods by treating tactile signals actively to model contact dynamics and enable explicit closed-loop control, moving beyond passive observation approaches.