In our approach, we introduce the Latent Structural Diffusion Model that enhances generative image modeling by integrating structural guidance and refining mechanisms for improved performance.
Our experiments demonstrate that the Structure-Guided Refiner significantly outperforms standard methods by effectively integrating multi-modal input conditions, resulting in more coherent and nuanced image generation.
The Human Verse Dataset we propose is critical for training and evaluating models that generate complex scenes, significantly contributing to advancements in the realm of computer vision.
Through extensive ablation studies, we reveal how variations in model conditioning, such as using depth maps or human poses, lead to varying degrees of output quality during the image synthesis process.
#image-generation #latent-structural-diffusion-model #computer-vision #ablation-studies #human-verse-dataset
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