DualCamCtrl: Dual-Branch Diffusion Model for Geometry-Aware Camera-Controlled Video Generation

This paper presents DualCamCtrl, a novel architecture forcamera-controlled video generation that internalizes geometric reason-ing into the diffusion process. Existing methods rely solely on ray-basedcamera conditioning, which entangles appearance and geometry model-ing within a single representation, or resort to external 3D cache andmulti-stage pipelines that are hard to jointly optimize. DualCamCtrladdresses this by generating depth as a co-evolving modality alongsideRGB through a dual-branch framework, enabling geometry and appear-ance to inform each other throughout denoising. To govern this cross-modal interaction, we propose the SemantIc Guided Mutual Alignment(SIGMA) mechanism, which schedules RGB–depth fusion in a stage-aware manner based on our empirical finding that depth and cameraposes exert asymmetric influence across the denoising trajectory. To-gether with a two-stage training strategy, these designs enable DualCam-Ctrl to disentangle and jointly evolve appearance and geometry, gener-ating videos that faithfully adhere to specified camera trajectories. Ex-tensive experiments demonstrate that DualCamCtrl achieves more con-sistent camera-controlled video generation with over 40% reductionon camera rotation errors compared with prior methods. Project Page:https://soyouthinkyoucantell.github.io/dualcamctrl-page.