Multi View Image Generation

Multi-view image generation aims to create multiple consistent views of an object or scene from a single input, often leveraging pre-trained diffusion models or GANs. Current research focuses on improving the realism and consistency of generated views, addressing challenges like pixel misalignment and error accumulation through techniques such as attention mechanisms, iterative warping and inpainting, and 3D-aware conditioning. This field is significant for advancing 3D reconstruction, novel view synthesis, and applications in areas like autonomous driving and virtual/augmented reality, where high-quality multi-view data is crucial.