World Rerenderer: Real-Time Camera-Controlled World Re-rendering via Video Diffusion Transformers

Real-time camera-controlled world re-rendering aims to synthesize target views from a streaming source video under online user-specified camera trajectories. This task poses two key challenges: maintaining synchronization and content consistency across views, and achieving low-latency rendering with long-horizon stability. Existing approaches are predominantly designed for offline full-sequence processing and rely on bidirectional attention or costly depth-based geometric preprocessing, making them unsuitable for low-latency streaming scenarios. To address these limitations, we develop a teacher-to-student framework that distills a high-fidelity bidirectional re-rendering teacher into a few-step causal autoregressive student. We introduce Cross-Frame Injection to interleave each incoming source frame with its synchronized target latent, thereby establishing direct cross-view correspondence and improving content consistency. To ensure stable long-horizon generation, we further propose Shared Relative RoPE, which bounds temporal indices within the pretrained range, and Cycle Streaming Tuning, which exposes the student to extended cyclic trajectories during streaming training and provides a closed-loop consistency signal. Together, these designs enable low-latency online video re-rendering with precise camera control and sustained spatiotemporal consistency over long streaming rollouts.