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New 3D generation method - 2×faster

New 3D generation method - 2×faster

Next step in text-to-3D

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Datasculptor
Nov 21, 2022
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MLearning.ai Art
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New 3D generation method - 2×faster
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3D digital content is used in gaming, entertainment, architecture, and robotics. Shopping, Internet conferencing, social networking, education, etc. are affected. Quality 3D requires creative, artistic, and modeling skills. This takes work. Natural language might help novices and experienced 3D artists. Text-to-3D models Diffusion approaches have enhanced image creation from text cues. Large Internet datasets (images with text) and computer capacity are key. Slower 3D content development. 3D objects are mostly categorized. These models are restrictive and not imaginative. This is because 3D material is less accessible online than photos and movies. It's unclear if text-to-image generative models can generate 3D. This article produces text-conditioned 3D content using a pre-trained text-to-image model. The 3D approach takes hours. This approach synthesizes 3D models from text quickly.
Prompt-based 3D Editing

Gaming, entertainment, architecture, and robotics simulation use 3D digital content. It's spreading to shopping, Internet conferencing, social media, education, etc. Creating quality 3D material involves creative, aesthetic, and 3D modeling knowledge. This requires time and effort. Augmenting 3D content production with natural language might aid beginners and skilled artists.

Generate realistic 3D models from text

Diffusion techniques for generative picture modeling have improved image production from text prompts. Large datasets (images with text) scraped from the Internet, and vast computational power are crucial facilitators. However, 3D content creation is substantially slower. Categorical models dominate 3D object creation.

These models are highly restricted and not suited for creative production. This is owing to the unavailability of large-scale 3D datasets – compared to images and videos, 3D material is less available online. This raises the issue of whether text-to-image generative models can accomplish 3D generation.

This article shows its capacity to produce text-conditioned 3D content using a pre-trained text-to-image model. The method produced 3D, but it takes hours.

The below method presents a technique for synthesizing 3D models from text in less time.

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