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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
No
API Access
Has API
No
Integrations
CodeQwen
Yes
Qwen 4
Yes
Qwen-7B
Yes
Qwen-Image
Yes
Qwen2
Yes
Qwen2-VL
Yes
Qwen2.5-1M
Yes
Qwen2.5-Coder
Yes
Qwen3
Yes
Qwen3.6
Yes
Integrations
CodeQwen
No
Qwen 4
No
Qwen-7B
No
Qwen-Image
No
Qwen2
No
Qwen2-VL
No
Qwen2.5-1M
No
Qwen2.5-Coder
No
Qwen3
No
Qwen3.6
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
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
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
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
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
Alibaba Cloud
Country
China
Website
modelscope.cn/
Vendor Details
Company Name
OpenAI
Founded
2015
Country
United States
Website
openai.com/research/point-e
