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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.

Description

Recent advancements in text-based 3D object generation have yielded encouraging outcomes; however, leading methods generally need several GPU hours to create a single sample, which is a stark contrast to the latest generative image models capable of producing samples within seconds or minutes. In this study, we present a different approach to generating 3D objects that enables the creation of models in just 1-2 minutes using a single GPU. Our technique initiates by generating a synthetic view through a text-to-image diffusion model, followed by the development of a 3D point cloud using a second diffusion model that relies on the generated image for conditioning. Although our approach does not yet match the top-tier quality of existing methods, it offers a significantly faster sampling process, making it a valuable alternative for specific applications. Furthermore, we provide access to our pre-trained point cloud diffusion models, along with the evaluation code and additional models, available at this https URL. This contribution aims to facilitate further exploration and development in the realm of efficient 3D object generation.

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
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
Step 3.5 Flash
Yi-Large

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
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

Alibaba Cloud

Country

China

Website

modelscope.cn/

Vendor Details

Company Name

OpenAI

Founded

2015

Country

United States

Website

openai.com/research/point-e

Alternatives

Alternatives

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Shap-E

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RODIN Reviews

RODIN

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