Novel view synthesis is a fundamental challenge in image-to-3D generation, requiring the generation of target view images from a set of conditioning images and their relative poses. While recent approaches like Zero-1-to-3 have demonstrated promising results using conditional latent diffusion models, they face significant challenges in generating consistent and accurate novel views, particularly when handling multiple conditioning images. In this work, we conduct a thorough investigation of Zero-1-to-3's cross-attention mechanism within the Spatial Transformer of the diffusion 2D-conditional UNet. Our analysis reveals a critical discrepancy between Zero-1-to-3's theoretical framework and its implementation, specifically in the processing of image-conditional context. We propose two significant improvements: (1) a corrected implementation that enables effective utilization of the cross-attention mechanism, and (2) an enhanced architecture that can leverage multiple conditional views simultaneously. Our theoretical analysis and preliminary results suggest potential improvements in novel view synthesis consistency and accuracy.