ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation

• URL: http://arxiv.org/abs/2603.09819v1
• Date: Tue, 10 Mar 2026 15:44:48 GMT
• Title: ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation
• Abstract summary: ConfCtrl is a confidence-aware video framework that enables camera-guided diffusion models to follow prescribed poses while completing unseen regions.<n>Experiments show that ConfCtrl produces geometrically consistent and visually plausible novel views, effectively reconstructing occluded regions under large viewpoint changes.
• Score: 24.89894187462497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address the challenge of novel view synthesis from only two input images under large viewpoint changes. Existing regression-based methods lack the capacity to reconstruct unseen regions, while camera-guided diffusion models often deviate from intended trajectories due to noisy point cloud projections or insufficient conditioning from camera poses. To address these issues, we propose ConfCtrl, a confidence-aware video interpolation framework that enables diffusion models to follow prescribed camera poses while completing unseen regions. ConfCtrl initializes the diffusion process by combining a confidence-weighted projected point cloud latent with noise as the conditioning input. It then applies a Kalman-inspired predict-update mechanism, treating the projected point cloud as a noisy measurement and using learned residual corrections to balance pose-driven predictions with noisy geometric observations. This allows the model to rely on reliable projections while down-weighting uncertain regions, yielding stable, geometry-aware generation. Experiments on multiple datasets show that ConfCtrl produces geometrically consistent and visually plausible novel views, effectively reconstructing occluded regions under large viewpoint changes.

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