Generative AI models are now part of our daily lives. They have advanced rapidly in recent years, and the results went from a funky image to a very photorealistic one relatively quickly. With all of these models like the MidJourney, StableDiffusion, and DALL-E, generating the image you have in mind has never been easier.
It’s not just 2D too. In the meantime, we have seen quite remarkable advances in the generation of 3D content. Whether the third dimension is time (video) or depth (NeRF, 3D models), the generated results get closer and closer to the real thing pretty quickly. These generative models have alleviated the requirement for 3D modeling and design expertise.
However, not everything is bright pink. 3D gens are getting more realistic, yes, but they’re still a long way behind 2D generative models. Large-scale text-to-image data sets have played a crucial role in expanding the capabilities of imaging algorithms. However, while 2D data is readily available, accessing 3D data for training and supervision is more challenging, resulting in a deficiency in 3D generative models.
The two main limitations of existing 3D generative models are lack of color saturation and low diversity compared to text-to-image models. let’s meet with Time to dream and see how it overcomes these limitations.
Time to dream shows that the limitations observed in the NeRF (Neural Radiance Fields) optimization process are mainly caused by the conflict between the uniform sampling of time steps in the scoring distillation. To address this conflict and overcome the limitations, he uses a novel approach that prioritizes the sampling of time intervals using monotonically non-increasing functions. By aligning the NeRF optimization process with the diffusion model sampling process, it is intended to improve the quality and efficiency of NeRF optimization to generate realistic 3D models.
Existing methods often result in models with saturated colors and limited diversity, which poses obstacles for content creation. To address this, Time to dream proposes a novel technique called time-prioritized punctuation-distillation sampling (TP-SDS) for the generation of 3D text. 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 time interval sampling strategy, TP-SDS aligns the text-to-3D optimization process with the diffusion model sampling process.
To assess the effectiveness of TP-SDS, the authors of Time to dream run full experiments and compare their performance to standard scoring distillation (SDS) sampling techniques. They analyze the conflict between optimizing text to 3D and uniform sampling of time intervals through mathematical formulations, gradient visualizations, and frequency analysis. The results demonstrate that the proposed TP-SDS approach significantly improves the quality and diversity of 3D text generation, surpassing existing methods.
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Ekrem Çetinkaya received his B.Sc. in 2018, and M.Sc. in 2019 from Ozyegin University, Istanbul, Türkiye. She wrote her M.Sc. thesis on denoising images using deep convolutional networks. She received her Ph.D. He graduated in 2023 from the University of Klagenfurt, Austria, with his dissertation titled “Video Coding Improvements for HTTP Adaptive Streaming Using Machine Learning.” His research interests include deep learning, computer vision, video encoding, and multimedia networking.
