The Sculpture of Dreams: DreamTime is an AI model that improves optimization strategy for generating text to 3D content

The Sculpture of Dreams: DreamTime is an AI model that improves optimization strategy for generating text to 3D content

Generative AI models are now part of our daily lives. They have progressed rapidly in recent years and the results have gone from a funky image to a highly photo-realistic image relatively quickly. With all these models like MidJourney, StableDiffusion and DALL-E, generating the image you have in mind has never been easier.

It’s not just 2D. Meanwhile, we have seen remarkable advances in the generation of 3D content. Whether the third dimension is time (video) or depth (NeRF, 3D models), the generated outputs are approaching the real ones quite rapidly. These generative models have facilitated proficiency requirements in 3D modeling and design.

However, not everything is bright pink. 3D generations are getting more realistic, yes, but they are still far behind 2D generative models. Large-scale text-to-image datasets have played a crucial role in expanding the capabilities of image-generation algorithms. However, while 2D data is readily available, accessing 3D data for training and supervision is more challenging, resulting in a shortage of 3D generative models.

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The two main limitations of existing generative 3D models are the lack of color saturation and the low diversity compared to text-image models. let’s meet up Dream time and see how it overcomes these limitations.

Dream time shows that the limitations observed in the NeRF (Neural Radiance Fields) optimization process are mainly caused by the conflict between the uniform timestep sampling in the score distillation. To address this conflict and overcome the limitations, it uses a new approach that prioritizes sampling of time steps using monotonically non-increasing functions. By aligning the NeRF optimization process with the diffusion model sampling process, we aim to improve the quality and effectiveness of NeRF optimization for generating realistic 3D models.

Existing methods often result in templates with saturated colors and limited diversity, putting obstacles in the way of content creation. To address this, Dream time proposes a new technique called time-priority score distillation sampling (TP-SDS) for text-to-3D generation. The key idea behind TP-SDS is to prioritize different levels of visual concepts provided by pre-trained diffusion models at various noise levels. This approach allows the optimization process to focus on refining details and improving visual quality. By incorporating a non-increasing timestep sampling strategy, TP-SDS aligns the text-to-3D optimization process with the diffusion pattern sampling process.

To evaluate the efficacy of TP-SDS, the authors of Dream time conduct comprehensive experiments and compare its performance with standard score distillation sampling (SDS) techniques. They analyze the conflict between text-to-3D optimization and uniform time-step sampling through mathematical formulations, gradient visualizations, and frequency analysis. The results demonstrate that the proposed TP-SDS approach significantly improves the quality and diversity of text-to-3D generation, surpassing existing methods.


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Ekrem Cetinkaya received his B.Sc. in 2018 and M.Sc. in 2019 at Ozyegin University, Istanbul, Trkiye. She wrote her M.Sc. thesis on image denoising using deep convolutional networks. She holds a PhD. he graduated in 2023 from the University of Klagenfurt, Austria with his thesis entitled “Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning”. His research interests include deep learning, computer vision, video coding and multimedia networking.


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