Two tracks in video diffusion have never quite connected: temporal autoregression pulls single-view to minute-level (Sora, Veo 3.1); bidirectional attention does multi-view consistency (VideoMV, 4Diffusion), but only stretches a few seconds of static content. Fiebelman et al. of Cornell Tech in arXiv:2607.05376 propose MV-Forcing: using autoregressive 3D reconstruction as a "4D geometric bridge" to transfer inter-view priors, letting a single diffusion model simultaneously take in "length" and "multi-view consistency". The mechanism is in three layers. The geometric bridge handles alignment — after the source view's 3D reconstruction, the depth, normal, and pose priors of the next view are rendered and handed to the diffusion for high-frequency details; 3D guards consistency, diffusion guards fidelity. Joint denoising lets both view slots start from noise and provide each other with priors, side-stepping the teacher's fixed window and making generation truly unbounded. DMD + Spatio-Temporal Self-Forcing distills a few-step student, with video-level loss patching exposure bias, continuing the Self Forcing line of Xun Huang's team, this time covering both temporal and view autoregression axes at the same time. Why it's worth watching. What world models and autonomous-driving simulation want is exactly "any length, any viewpoint, physical consistency" 4D scenes, which previously could only be achieved by stacking compute or splicing multi-channel pipelines. MV-Forcing proposes a lightweight paradigm of "geometry to 3D, texture to diffusion" — if validated by larger models, the cost curve of industrial-grade 4D video generation is expected to drop another notch.