Training-free, camera-controlled novel view synthesis from a single image using pre-trained video diffusion models often becomes unstable under large camera motion and long generation horizons. Existing approaches commonly combine several inference-time components, making it unclear which design choices are most important for stability. This work identifies the amount of camera motion handled by each diffusion pass as the main factor. CamTrol++ splits the camera path into passes that each cover at most a fixed rotation and generates them one after another, so that every pass stays within the range of motion the backbone handles well. A matched study, in which every setting is scored on the same poses of the same $60^\circ$ orbit, shows that geometric error grows steadily with the arc per pass, while a single SVD pass both loses geometry and tends to freeze the camera. Under one shared camera path, CamTrol++ achieves the lowest epipolar error and the best held-out 3D Gaussian Splatting reconstruction among CamTrol, NVS-Solver and WAVE, and reaches the quality of CamTrol with the same pass structure at least $3.2\times$ faster. On real RealEstate10K motion it matches CamTrol at half the run time (NVS-Solver is more faithful but $8\times$ slower). The same procedure also improves 3D reconstruction with a second video backbone, LTX-Video. Geometry-constrained spatial attention, color anchoring at pass boundaries and a registration-free warping pipeline that makes warping $6.1\times$ faster complete the framework. The method remains effective for 56-frame generation and under controlled depth corruption. These results show that careful control of camera motion at inference time can substantially improve the stability of camera-controlled novel view synthesis.

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