Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
HunyuanVideo-Avatar allows for the transformation of any avatar images into high-dynamic, emotion-responsive videos by utilizing straightforward audio inputs. This innovative model is based on a multimodal diffusion transformer (MM-DiT) architecture, enabling the creation of lively, emotion-controllable dialogue videos featuring multiple characters. It can process various styles of avatars, including photorealistic, cartoonish, 3D-rendered, and anthropomorphic designs, accommodating different sizes from close-up portraits to full-body representations. Additionally, it includes a character image injection module that maintains character consistency while facilitating dynamic movements. An Audio Emotion Module (AEM) extracts emotional nuances from a source image, allowing for precise emotional control within the produced video content. Moreover, the Face-Aware Audio Adapter (FAA) isolates audio effects to distinct facial regions through latent-level masking, which supports independent audio-driven animations in scenarios involving multiple characters, enhancing the overall experience of storytelling through animated avatars. This comprehensive approach ensures that creators can craft richly animated narratives that resonate emotionally with audiences.
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
Yes
API Access
Has API
No
Integrations
CodeQwen
No
Gradio
Yes
Qwen
No
Qwen 4
No
Qwen2
No
Qwen2-VL
No
Qwen2.5-1M
No
Qwen2.5-Coder
No
Qwen2.5-Max
No
Qwen2.5-VL
No
Integrations
CodeQwen
Yes
Gradio
No
Qwen
Yes
Qwen 4
Yes
Qwen2
Yes
Qwen2-VL
Yes
Qwen2.5-1M
Yes
Qwen2.5-Coder
Yes
Qwen2.5-Max
Yes
Qwen2.5-VL
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
Yes
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
Tencent-Hunyuan
Country
United States
Website
github.com/Tencent-Hunyuan/HunyuanVideo-Avatar
Vendor Details
Company Name
Alibaba Cloud
Country
China
Website
modelscope.cn/