Ctrl123: Consistent Novel View Synthesis via Closed-Loop Transcription
Hongxiang Zhao Xili Dai Jianan Wang Shengbang Tong Jingyuan Zhang Weida Wang Lei Zhang Yi Ma
Large image diffusion models have demonstrated zero-shot capability in novel
view synthesis (NVS). However, existing diffusion-based NVS methods struggle to
generate novel views that are accurately consistent with the corresponding
ground truth poses and appearances, even on the training set. This consequently
limits the performance of downstream tasks, such as image-to-multiview
generation and 3D reconstruction. We realize that such inconsistency is largely
due to the fact that it is difficult to enforce accurate pose and appearance
alignment directly in the diffusion training, as mostly done by existing
methods such as Zero123. To remedy this problem, we propose Ctrl123, a
closed-loop transcription-based NVS diffusion method that enforces alignment
between the generated view and ground truth in a pose-sensitive feature space.
Our extensive experiments demonstrate the effectiveness of Ctrl123 on the tasks
of NVS and 3D reconstruction, achieving significant improvements in both
multiview-consistency and pose-consistency over existing methods.