Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?

Recent advancements in deep generative models, particularly with the application of CLIP (Contrastive Language Image Pretraining) to Denoising Diffusion Probabilistic Models (DDPMs), have demonstrated remarkable effectiveness in text to image generation. The well structured embedding space of CLIP has also been extended to image to shape generation with DDPMs, yielding notable results. Despite these successes, some fundamental questions arise: Does CLIP ensure the best results in shape generation from images? Can we leverage conditioning to bring explicit 3D knowledge into …

application clip cs.ai cs.cv deep generative models denoising diffusion embedding embeddings generative generative models image image generation language pretraining space text