VEED Fabric 1.0

Model Overview

VEED Fabric 1.0 is a state-of-the-art generative AI model designed to transform static images into realistic talking videos with expressive, lip-synced, and emotion-rich animated characters. It supports a broad range of image styles from photos to illustrations and mascots and synchronizes mouth, facial expressions, head, and body movements with an audio voice input. Fabric 1.0 notably advances video creation by being significantly faster and cheaper than prior solutions, making it ideal for creators, marketers, and enterprises seeking cost-effective video production at scale.

Technical Specifications

• Architecture: Diffusion Transformer (DiT)
• Input image formats: jpg, jpeg, png, webp, gif, avif (max 10MB)
• Audio input formats: mp3, ogg, wav, m4a, aac (max 10MB)
• Video output format: mp4
• Supported resolutions: 480p and 720p (16:9 aspect ratio) plus others like 1:1, 4:3, 3:4, 9:16 with scaled resolutions
• Frame rate: 25 FPS
• Maximum video length: 60 seconds

Performance Benchmarks

• Approximately 7x faster generation speed than average image-to-video models
• High fidelity lip synchronization and expressive motion reduce unnatural or stiff animations
• Supports complex dialogues and emotionally rich speech

Key Features of VEED Fabric 1.0

• Transforms any static image (photo, illustration, character render) into a realistic talking video
• High-quality lip-sync synchronized precisely with the provided audio input
• Natural facial expressions, including eye movements, head nods, and body gestures
• Supports multiple video aspect ratios including 16:9, 1:1, 9:16 with resolutions of 480p and 720p
• Video output in widely compatible MP4 format at 25 FPS
• Fast video generation: around 1.5 minutes per 10 seconds of 480p video
• Uses advanced diffusion-transformer architecture for expressive, natural movement

API Pricing

• 480p: $0.104;
• 720p: $0.195

Use Cases

• Explainer and educational videos: Create engaging face-to-camera presentations from text or blog content for online learning and tutorials
• Marketing and social media: Produce branded talking videos and ad variations quickly and cost-effectively for platforms like TikTok, Instagram, and YouTube Shorts
• Animated mascots and characters: Bring brand mascots or fictional characters to life without manual animation pipelines
• Personalized video at scale: Generate customized messages for different audience segments automatically
• Internal and external corporate communication: Use AI avatars as spokespeople or for training materials without the need for video shoots
• Content creation for creators and influencers: Quickly produce polished talking head videos without filming, reducing production time and costs drastically

Generation Code Sample

Output Code Sample

Comparison with Other Models

vs Kling AI Avatar: VEED Fabric 1.0 offers faster generation speeds and cost-efficient production for marketers and educators, with high fidelity lip-sync and natural gestures. Kling AI Avatar focuses more on cinematic realism and emotional depth, ideal for storytellers seeking highly nuanced character expressions.

vs Synthesia: VEED Fabric 1.0 animates any static image with natural lip-sync and expressive gestures, supporting diverse input styles and longer videos. Synthesia primarily offers a library of preset avatars for corporate and educational videos with more limited creative input flexibility.

vs HeyGen: VEED Fabric excels in flexibility of input images and faster generation speeds, suited for marketing, creators, and educators requiring multiple video variations quickly. HeyGen provides high-fidelity digital avatars with a focus on localized languages and interactive dialogue systems for advanced virtual communication.

vs Hour One: VEED Fabric offers broad creative freedom by animating any static image plus integrated editing tools for fast content workflows. Hour One is more focused on enterprise virtual spokespersons and language synthesis integration for automated corporate videos.

API Integration

Accessible via AI/ML API. Documentation: available here.