The field of computer vision is witnessing significant developments in multi-view image generation and editing, with a focus on improving cross-view consistency, detail preservation, and realism. Researchers are exploring innovative approaches to address the challenges of maintaining shape and structural consistency across different views, generating high-resolution outputs, and ensuring realistic results. Notable advancements include the use of geometric information extraction, decoupled geometry-enhanced attention mechanisms, and adaptive learning strategies to fine-tune models and capture spatial relationships. Furthermore, novel evaluation frameworks are being proposed to assess the reliability and faithfulness of generated images. Some noteworthy papers in this area include: GeoMVD, which incorporates geometric information extraction and decoupled geometry-enhanced attention mechanisms to generate consistent and detailed images. LSS3D, which proposes a learnable spatial shifting approach to handle multi-view inconsistencies and non-frontal input views, resulting in high-quality 3D generation with complete geometric details and clean textures. Appreciate the View, which introduces a task-aware evaluation framework to assess the reliability and faithfulness of generated images, providing a principled and practical approach to evaluating synthesis quality.