The integration of diffusion models across various research areas has catalyzed significant advancements in image generation, 3D modeling, and human-object interaction detection. In image restoration and super-resolution, diffusion models have enabled faster, more personalized processes by incorporating single-step mechanisms and attention-sharing techniques. These innovations have addressed diverse degradation types and real-time processing demands, while also improving semantic consistency and perceptual naturalness. In 3D scene editing, diffusion models have introduced 3D consistency into image inpainting, ensuring realistic and coherent results across different viewpoints. This is achieved through techniques that integrate alternative perspectives into the denoising process, creating an inductive bias for 3D priors without explicit supervision. The field of 3D generation has also seen progress, with researchers leveraging 2D diffusion models to enhance realism and diversity in 3D outputs. The incorporation of tactile sensing and novel texture generation frameworks have refined geometric details, while generative geolocation methods have improved accuracy through probabilistic models and interactive elements. In human-object interaction detection, advancements in prompt distribution learning and spatial context learning have enhanced the representation of diverse intra-category patterns and inter-category dependencies, leading to more robust and versatile recognition systems. Notable developments include a unified model for 3D human-object interactions and the synergy between 2D and 3D diffusion models for realistic image-to-3D generation. These innovations collectively represent a substantial leap forward, offering scalable solutions and state-of-the-art performance across various applications.