Machine Learning Art
Text-to-3D Generation
Can AI create 3D models? [update August 2023]
Does text-to-3D modeling have a future?
The future is an exciting place for the 3D model, in how the public and professionals alike are going to be able to interact with them and create them. While there is already some excellent text to 3D modeling tools available, the future will see even more amazing things. One of the most exciting things that we will see is the ability to create 3D models from natural language descriptions.
🟠 State of the 3D Art [June 2023]
- August 2023 — AI art 3D tools update can be found ➡️ HERE ⬅️
3D Midjourney: An Exciting Announcement and Invitation to the Future of Art
The Power of 3D Modeling with AI
Natural language-based 3D modeling can open up new possibilities for visualizing and shaping the world around us. However, while there has been substantial development in text-to-image creation in recent years, text-to-form generation remains a complex topic due to the scarcity of coupled text and shape data on a broad scale.
The authors offer a basic yet practical zero-shot text-to-shape creation approach that avoids data scarcity. CLIP-Forge, the suggested technique, is based on a two-stage training procedure that requires just an unlabeled shape dataset and a pre-trained image-text network like CLIP. The technique does not require time-consuming inference optimization and the flexibility to produce various forms for a single text.
Text-to-shape generation models are a significant enabler for new innovative tools in creative design and manufacturing and animation and gaming in practice.
Project Page + Github (scroll down)
🟠 The following are the method’s significant contributions:
🔵 The authors introduce CLIP-Forge, a novel approach for generating 3D forms directly from text without the need for coupled text-shape labels.
🔵 Their paper provides an extensive qualitative and quantitative evaluation of their method in various zero-shot generation settings.
🔵 Their method has an efficient generation process that requires no inference time optimization, can generate multiple shapes for a given text, and be easily extended to multiple 3D representations.
The fundamental notion is depicted graphically. Due to a paucity of matched data, learning text-to-shape creation directly is challenging. To bridge the data gap between 3D forms and spoken language, the authors utilize shape renderings with a pre-trained image-text joint embedding model.
🟠 Instant 3D Worlds & Camera-Free Movies.
CLIP Architecture:
What exactly is CLIP? CLIP is OpenAI’s first multimodal (in this instance, 3D and text) computer vision model, launched on January 5, 2021.
Contrastive Language-Image Pre-Training — a neural network trained on various (image, text) pairings. Similar to the zero-shot capabilities of GPT-2 and 3, it may be told in natural language to anticipate the best appropriate text fragment given an image without directly optimizing for the job. Without utilizing any of the original 1.28M labeled examples, CLIP equals the performance of the original ResNet50 on ImageNet “zero-shot,” addressing many significant difficulties in computer vision.
Conclusion
CLIP-Forge is a technique that efficiently generates various 3D forms while maintaining the semantic meaning of a text prompt. Furthermore, the technique does not require text-shape labels as training data, allowing shape-only datasets like ShapeNet. Finally, the model can provide results on other representations, such as point clouds.
Machine Learning can transform a collection of 2D photos into a 3D
How can I turn a picture into a 3D model?
title:CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation
the authors: Aditya Sanghi, Hang Chu, Joseph G. Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero, Kamal Rahimi Malekshan
Project page:
https://arxiv.org/pdf/2110.02624.pdf
Github:
https://github.com/AutodeskAILab/Clip-Forge
Keywords: computer vision, Artificial Intelligence, datasets, Machine Learning, AI art, art, digital art, 3D, generative, 3D modeling, text-to-shape, text-to-3D, CLIP-Forge, Clip, 3D world, have i been trained
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