to download project base paper on learning machine learning of text-to-image projects.

Abstract:

Score distillation sampling (SDS) has shown great promise of Machine learning in text to image-3D generation by distilling pre-trained large-scale text-to-image diffusion models but suffers from over-saturation, over-smoothing, and low-diversity problems. In this work, we propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational score distillation (VSD), a principled particle-based variational framework to explain and address the aforementioned issues in text-to-3D generation. We show that SDS is a special case of VSD and leads to poor samples with both small and large CFG weights. In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i.e., 7.5). We further present various improvements in the design space for text-to-3D such as distillation schedule and density initialization, which are orthogonal to the distillation algorithm yet not well explored. Our overall approach, dubbed ProlificDreamer, can generate high rendering resolution (i.e., 512×512) and high-fidelity NeRF with rich structure and complex effects (e.g., smoke and drops).

3D content and technologies enable us to visualise, comprehend, and interact with complex objects and environments that are reflective of our real-life experiences. Their pivotal role extends across a wide array of domains, encompassing architecture, animation,gaming, and equally evolving fields of virtual augmented reality. Despite the extensive applications, the production of premium 3D content often remains a formidable task. It necessitates a significant investment of time and effort, even when undertaken by professional designers. By automating the generation of 3D-based descriptions, these innovative methods present a promising way toward streamlining the 3D content creation process. Furthermore, they stand to make this process more accessible, and potentially encouraging. This challenge has prompted the development of text-to-3DmethodsFurther, initialized from NeRF, meshes fine-tuned by VSD are meticulously detailed and photo-realistic. Project page and codes: Click Here

PROLIFICDREAMER: HIGH-FIDELITY AND DIVERSE TEXT-TO-3D GENERATION WITH VARIATIONAL SCORE DISTILLATION- text to image Projects  in Visakhapatnam, Hyderabad
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