Object Generation

Object generation, encompassing the creation of images and 3D models from various inputs like text or shapes, aims to produce realistic and semantically consistent outputs. Current research heavily utilizes diffusion models, often enhanced with techniques like attention mechanisms, adapter training, and novel loss functions to improve control over object placement, attributes, and overall composition, particularly in multi-object scenes. These advancements are driving progress in diverse applications, including image editing, virtual and augmented reality, and the creation of synthetic datasets for training other AI models. The focus is on improving both the fidelity and controllability of generated objects, addressing challenges like object repetition and ensuring alignment between textual descriptions and visual outputs.