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.
@article{arxiv.2603.09819,
title = {ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation},
author = {Liudi Yang and George Eskandar and Fengyi Shen and Mohammad Altillawi and Yang Bai and Chi Zhang and Ziyuan Liu and Abhinav Valada},
journal= {arXiv preprint arXiv:2603.09819},
year = {2026}
}