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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 No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

01.AI No 
CodeQwen No 
GLM-4.5 No 
Qwen-Image No 
Qwen2 No 
Qwen2.5 No 
Qwen2.5-Coder No 
Qwen2.5-Max No 
Qwen2.5-VL No 
Qwen3 No 
Qwen3.6 No 
Qwen3.6-27B No 
Qwen3.6-Max-Preview No 
Qwen3.7-Max No 
Qwen3.7-Plus No 
Qwen3.8-2.4T-A95B No 
Qwen3.8-27B No 
Qwen3.8-Omni-Flash No 
Step 3.5 Flash No 
Yi-Large No 

Integrations

01.AI Yes 
CodeQwen Yes 
GLM-4.5 Yes 
Qwen-Image Yes 
Qwen2 Yes 
Qwen2.5 Yes 
Qwen2.5-Coder Yes 
Qwen2.5-Max Yes 
Qwen2.5-VL Yes 
Qwen3 Yes 
Qwen3.6 Yes 
Qwen3.6-27B Yes 
Qwen3.6-Max-Preview Yes 
Qwen3.7-Max Yes 
Qwen3.7-Plus Yes 
Qwen3.8-2.4T-A95B Yes 
Qwen3.8-27B Yes 
Qwen3.8-Omni-Flash Yes 
Step 3.5 Flash Yes 
Yi-Large Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

DreamFusion

Website

dreamfusion3d.github.io

Vendor Details

Company Name

Alibaba Cloud

Country

China

Website

modelscope.cn/

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