Image to 3D Generation

Image-to-3D generation aims to create realistic three-dimensional models from two-dimensional images, leveraging advancements in deep learning. Current research focuses on improving both the quality and efficiency of this process, exploring architectures like diffusion models (often incorporating multi-view or single-view approaches) and variational autoencoders, sometimes combined with neural radiance fields (NeRFs) for enhanced realism. These methods are improving the speed and fidelity of 3D model creation, with significant implications for fields such as computer graphics, virtual and augmented reality, and digital art. The development of scalable and high-quality image-to-3D generation techniques is driving progress in various applications requiring realistic 3D content.