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features
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Description

Recent advancements in the realm of text-to-image synthesis have emerged from diffusion models that have been trained on vast amounts of image-text pairs. To successfully transition this methodology to 3D synthesis, it would necessitate extensive datasets of labeled 3D assets alongside effective architectures for denoising 3D information, both of which are currently lacking. In this study, we address these challenges by leveraging a pre-existing 2D text-to-image diffusion model to achieve text-to-3D synthesis. We propose a novel loss function grounded in probability density distillation that allows a 2D diffusion model to serve as a guiding principle for the optimization of a parametric image generator. By implementing this loss in a DeepDream-inspired approach, we refine a randomly initialized 3D model, specifically a Neural Radiance Field (NeRF), through gradient descent to ensure its 2D renderings from various angles exhibit a minimized loss. Consequently, the 3D representation generated from the specified text can be observed from multiple perspectives, illuminated with various lighting conditions, or seamlessly integrated into diverse 3D settings. This innovative method opens new avenues for the application of 3D modeling in creative and commercial fields.

Description

This system utilizes a sophisticated multi-stage diffusion model for converting text descriptions into corresponding video content, exclusively processing input in English. The framework is composed of three interconnected sub-networks: one for extracting text features, another for transforming these features into a video latent space, and a final network that converts the latent representation into a visual video format. With approximately 1.7 billion parameters, this model is designed to harness the capabilities of the Unet3D architecture, enabling effective video generation through an iterative denoising method that begins with pure Gaussian noise. This innovative approach allows for the creation of dynamic video sequences that accurately reflect the narratives provided in the input descriptions.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

01.AI
CodeQwen
GLM-4.5
Qwen
Qwen-7B
Qwen-Image
Qwen2
Qwen2-VL
Qwen2.5
Qwen2.5-1M
Qwen2.5-Coder
Qwen2.5-Max
Qwen2.5-VL
Qwen3
Qwen3.6
Qwen3.6-27B
Qwen3.6-35B-A3B
Qwen3.6-Max-Preview
Step 3.5 Flash
Yi-Large

Integrations

01.AI
CodeQwen
GLM-4.5
Qwen
Qwen-7B
Qwen-Image
Qwen2
Qwen2-VL
Qwen2.5
Qwen2.5-1M
Qwen2.5-Coder
Qwen2.5-Max
Qwen2.5-VL
Qwen3
Qwen3.6
Qwen3.6-27B
Qwen3.6-35B-A3B
Qwen3.6-Max-Preview
Step 3.5 Flash
Yi-Large

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

DreamFusion

Website

dreamfusion3d.github.io

Vendor Details

Company Name

Alibaba Cloud

Country

China

Website

modelscope.cn/

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