"美国大学扩散模型讲义及图像生成模型探究"

Lecture X at one of the prestigious universities in the United States covered the topic of Denoising Diffusion Models. The instructor, who had only recently familiarized themselves with this subject matter, led the class in exploring the intricate details of how diffusion models can be utilized in various applications. The upcoming lectures were planned to delve into generative models for direct image-based rendering, focusing on 3D intrinsic components in the realms of vision and graphics editing. The crux of the discussion revolved around the transformation of current images into new ones under different conditions, including changes in viewpoint, lighting, reflectance, background, attributes, and numerous other factors. This transformation was achieved by leveraging implicit methods, particularly utilizing neural networks such as Conditional Generative Networks, often implemented in an end-to-end fashion. The mechanism behind these generative models lies in the latent space, which facilitates the encoding and decoding of information crucial for generating new images. The process involves complex encoder-decoder architectures that enable the manipulation of images to produce desired outcomes. Through innovative techniques and advancements in the field of generative models, researchers and practitioners can explore new frontiers in image manipulation and rendering.