DreamActor-M1 From ByteDance
DreamActor-M1 is ByteDance's advanced AI framework that transforms static human images into highly expressive and realistic animated videos through hybrid guidance, supporting everything from facial expressions to full-body movements with robust temporal consistency.
https://grisoon.github.io/DreamActor-M1?ref=aipure
Product Information
Updated:Jun 16, 2025
DreamActor-M1 From ByteDance Monthly Traffic Trends
DreamActor-M1 from ByteDance achieved 45,968 visits with a 2049.0% growth in the given period. This significant increase is likely due to the product's recent launch or a major marketing push, as there are no specific updates or news directly related to the product.
What is DreamActor-M1 From ByteDance
DreamActor-M1 is a groundbreaking diffusion transformer (DiT) based human animation framework developed by ByteDance. Released in 2025, it represents a significant advancement in AI-powered video generation technology, addressing critical challenges in fine-grained holistic controllability, multi-scale adaptability, and long-term temporal coherence. The framework can animate a single reference image into dynamic videos, maintaining high fidelity in facial expressions, body movements, and identity preservation across multiple scales - from portraits to full-body animations. As ByteDance's latest innovation in generative AI, DreamActor-M1 directly challenges existing tools like Runway's Act-One by offering superior detail preservation and animation quality.
Key Features of DreamActor-M1 From ByteDance
DreamActor-M1 is ByteDance's advanced AI animation framework that transforms static human images into dynamic, expressive videos using hybrid guidance technology. Built on Diffusion Transformers (DiT), it achieves fine-grained control over facial expressions, body movements, and lip synchronization while maintaining temporal coherence and identity preservation across multiple scales - from portraits to full-body animations. The framework integrates implicit facial representations, 3D head spheres, and 3D body skeletons to produce highly realistic and consistent animations.
Hybrid Guidance System: Combines implicit facial representations, 3D head spheres, and 3D body skeletons to achieve precise control over facial expressions and body movements while maintaining identity preservation
Multi-scale Adaptability: Handles various body poses and image scales from portraits to full-body views through progressive training strategy with varying resolutions
Long-term Temporal Coherence: Ensures consistent appearance and movement across extended video sequences by integrating motion patterns from sequential frames with complementary visual references
Shape-aware Animation: Utilizes bone length adjustment techniques to adapt animations to different body characteristics and maintain anatomical accuracy
Use Cases of DreamActor-M1 From ByteDance
Digital Content Creation: Enables content creators to generate dynamic video content from single images, revolutionizing social media and digital storytelling
Film Production: Assists filmmakers in creating animated sequences and character animations from still photographs, reducing production costs and time
Virtual Reality and Education: Creates immersive educational content and interactive experiences by animating static characters for VR environments
Multi-language Content: Supports audio-driven facial animation for multiple languages, enabling efficient localization of video content
Pros
Superior detail preservation in facial expressions and body movements
Strong temporal consistency for long-duration videos
Versatile application across different scales and scenarios
Cons
Potential ethical concerns regarding deepfake creation
Unclear training data sources and commercial availability
May require significant computational resources for processing
How to Use DreamActor-M1 From ByteDance
Note: No Direct Usage Instructions Available: Based on the provided sources, DreamActor-M1 appears to be a research project by ByteDance that has been announced but not yet released for public use. There are no explicit instructions or tutorials available for using the tool at this time.
Basic Concept: DreamActor-M1 is designed to animate a single reference image of a person using motion guidance from a driving video. It can generate portraits, upper-body, and full-body animations with facial expressions and body movements.
Theoretical Input Process: When available, the system would likely require: 1) A reference image of the person you want to animate, 2) A driving video containing the desired motion/expressions to transfer
Current Status: The model is currently only described in research papers and demonstrations. While there is a GitHub repository (grisoon/DreamActor-M1), it appears to only contain the project page rather than usable code.
Future Availability: Interested users should monitor ByteDance's official channels and the project's GitHub page (https://grisoon.github.io/DreamActor-M1/) for any future public release or usage instructions.
DreamActor-M1 From ByteDance FAQs
DreamActor-M1 is a diffusion transformer (DiT) based framework developed by ByteDance that enables holistic human image animation with hybrid guidance. It can create realistic human videos from a reference image by imitating behaviors captured from videos, ranging from portrait to full-body animations.
DreamActor-M1 From ByteDance Video
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Analytics of DreamActor-M1 From ByteDance Website
DreamActor-M1 From ByteDance Traffic & Rankings
9.3K
Monthly Visits
#2385664
Global Rank
#13136
Category Rank
Traffic Trends: Feb 2025-Jun 2025
DreamActor-M1 From ByteDance User Insights
00:00:02
Avg. Visit Duration
1.28
Pages Per Visit
38.7%
User Bounce Rate
Top Regions of DreamActor-M1 From ByteDance
US: 22.44%
IN: 11.75%
TH: 6.82%
ID: 5.34%
BG: 4.67%
Others: 48.97%
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