Alibaba Introduces Champ to Generate Lifelike Human Animations Using 3D Models
In the world of animation, creating lifelike human characters has always been a challenge. Recent advances in generative diffusion models have enabled compelling human image animation. However, existing techniques often struggle with shape alignment and motion guidance. To address these challenges, researchers from Nanjing University, Fudan University and Alibaba Group have introduced Champ. In this article, we will delve into its details
Champ is a novel framework for human image animation that leverages 3D parametric modelling to enhance shape representation and motion guidance. This approach utilizes the SMPL model, a popular 3D parametric human model. Moreover, SMPL represents the human form using shape and pose parameters in a low-dimensional space. By establishing correspondence between reference and driving motion through SMPL, Champ can accurately capture intricate geometry and motion characteristics while aligning 3D shapes.
Example Human Animations Generated by Champ
Key Methodology of CHAMP
Its methodology consists of three key components:
1. SMPL Motion Extraction
The SMPL model fitted to reference and driving video extracts pose parameter sequences. These are projected to generate multi-layer maps carrying 3D structural information.
2. Parametric Shape Alignment
SMPL models from reference and driving video are aligned in parameter space to transfer shapes accurately across identities during animation.
3. Multi-Layer Motion Guidance
Generated maps alongside skeleton guidance are encoded with self-attention and then fused as multi-level embeddings, conditioning the diffusion process for precise animation.
Combining CHAMP with T2I
CHAMP integrates techniques from text-to-image generation models like Stable Diffusion by conditioning on CLIP embeddings of the reference image. This allows animations to be controlled not just with video motion but also with natural language descriptions.
Prompt: A woman in a silver dress posing for a picture, trending on cg society, futurism, with bright blue eyes, dior campaign, tesseract, miranda kerr –v 5. 1 –ar 3:4. (Credits:Champ by Alibaba)
Training Details
The training dataset includes over 5,000 high-fidelity human videos sourced from popular sources with over 1 million frames. This dataset features large variations to train a robust model. The model was trained using 8 NVIDIA A100 GPUs in two phases – first processing individual frames and then adding a temporal layer for coherent video generation.
Performance Evaluation of CHAMP
1. Comparisons with Existing Approaches
Extensive experiments compare CHAMP against state-of-the-art methods like DisCo, MagicAnimate and AnimateAnyone. Significantly, it surpasses these diffusion-based techniques in metrics like PSNR, SSIM and LPIPS, demonstrating visually sharper and more consistent results.
When tested on the standard TikTok dataset, it outperforms baselines in all evaluated metrics, from L1 loss to FID and FVD scores. Fine-tuning it on this data further improves performance.
3. Unseen Domain Animation
To assess generalization, CHAMP animates a new and more diverse “wild” dataset. It continues producing high-quality videos and exceeds competitors in terms of LPIPS and FVD, showing strong cross-domain abilities.
4. Cross-ID Animation
It can also generate animations where the reference image person differs from those in the driving videos. Aligning shapes parametrically maintains identity coherence better than pose-only approaches.
Conclusion
Champ by Alibaba researchers introduces a novel approach for generating lifelike human animation leveraging 3D parametric modeling. It addresses current limitations and produces controllable, consistent animations of human characters. Last but not least, CHAMP sheds new light on unlocking the full potential of human image animation. For code and installation details, you can visit the GitHub repository at fudan-generative-vision/champ. Additionally, to learn more technical details, please visit the project arXiV paper by Alibaba!
Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!
AI Slop Is Brute Forcing the Internet’s Algorithms for Views
Imagine a digital world where fake videos, images, and posts flood your favorite social media platforms like an unstoppable wave. Welcome to the crazy universe of AI Slop – a digital phenomenon that’s completely transforming how we experience the internet. AI Slop is a massive brute-force attack that’s rewriting the rules of online content creation. It’s found a way to trick social media platforms into showing its videos to millions of people.
AI Slop isn’t your typical online content. It’s a wild, AI-generated flood of videos created with one primary goal: tricking social media algorithms into giving it maximum visibility. No creativity, no real purpose, just pure algorithmic chaos. These videos are generated in seconds or minutes, with some accounts posting multiple times per day across various platforms.
Social media platforms have secret recipes called algorithms that decide what videos and posts you see. Understanding how AI Slop works requires diving into the world of algorithmic manipulation. AI Slop relies on advanced machine learning algorithms that can analyze millions of successful content pieces and generate similar, attention-grabbing material in seconds.
Normally, creating great content takes time. Content creators might spend days or weeks on a single project. But AI Slop creators can generate hundreds of videos in just minutes.
The Brute Force Attack on Internet Algorithms
Remember how, in spy movies, hackers try every possible password combination? AI Slop works almost exactly the same way. Social media platforms have complex recommendation systems designed to keep users glued to their screens. AI Slop has discovered a critical vulnerability: these algorithms care more about engagement than actual content quality.
Instead of trying to break into a computer system, these digital creators are breaking into recommendation systems by flooding platforms with content. By continuously producing content, these AI systems eventually crack the code of what makes algorithms tick.
A Reporter’s Shocking AI Slop Discovery
Meet Jason Kebler, a reporter for 404 Media who stumbled upon a mind-blowing digital phenomenon. His Instagram feed became a bizarre showcase of AI-generated videos that defy imagination. He explains how such weird AI-generated videos get viewed millions of times!
Kebler’s daily experience became a front-row seat to the AI Slop revolution. His Instagram Reels were packed with strange, often grotesque AI-generated videos that seemed to multiply faster than anyone could comprehend. These weren’t just random clips – they were strategic attempts to hack social media algorithms.
Content creators are discovering a shocking truth: quantity now trumps quality in the digital ecosystem. Some claim it’s pointless to spend time creating high-quality videos when AI can do 90% of the work in minutes. They say users can create 8-10 AI-generated videos in just 30 minutes, arguing that platforms like YouTube are “hungry to feed their audience.”
The Disturbing Engagement Mechanism
Here’s the most shocking part of Kebler’s investigation: these AI Slop videos actually work. When users interact with AI Slop even negatively, the algorithm interprets this as a positive signal. Commenting, watching, or even slowly scrolling past an AI Slop video tells the system, “Hey, this content is interesting!”
Platform Perspectives on AI Slop
Major tech companies seem more intrigued than concerned. Surprisingly, platforms like Instagram and TikTok aren’t fighting this trend. Meta’s CEO Mark Zuckerberg has suggested that AI-generated content could create “entirely new categories” of user engagement.
Platforms like Meta are developing AI tools that help advertisers generate multiple ad versions, indicating they see generative AI as an opportunity rather than a threat.
Real-World Implications of AI Slop
Kebler warns of a potential future where AI Slop becomes hyper-personalized. Imagine AI-generated videos about golden retrievers recommended to dog owners or conspiracy theory videos targeting specific belief groups. As AI Slop continues to spread, we’re witnessing a massive transformation of our online information landscape. Human creativity is at serious risk of being completely overshadowed by machine-generated content.
Protecting Yourself in the AI Slop Era
As AI Slop becomes more sophisticated, important questions arise about digital authenticity, creativity, and the future of online content. With AI Slop flooding platforms, distinguishing between real and generated content becomes increasingly challenging. Users might soon struggle to determine what’s authentic.
Digital literacy is becoming crucial. Understanding how AI Slop works can help users navigate this new landscape more intelligently. Look for repetitive content, unnaturally perfect visuals, and videos that seem slightly “off” – these might be telltale signs of AI-generated material.
Wrapping Up
AI Slop isn’t just a trend – it’s a complete transformation of how we create and consume online content. It’s challenging everything we know about creativity, marketing, and technology. The brute force attack on internet algorithms will likely become even more sophisticated.
We’re watching a digital revolution unfold – one bizarre, algorithm-beating video at a time. Buckle up because the internet is about to get a whole lot weirder.
Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!
Texas School Uses AI Tutor to Rocket Student Scores to the Top 2% in the Nation
Ever wondered how technology could change the classroom experience? Imagine if students could study for just two hours a day and still rank in the top 2% nationwide. Sounds impossible, right? Well, that’s exactly what’s happening at Alpha School, a private Texas school that has integrated an AI tutor into its curriculum, as reported by Fox News. The results? Students are learning faster and better than ever before.
Alpha School, based in Austin, Texas, has taken a bold approach by using an AI tutor to personalize education for each student. Most schools follow a one-size-fits-all approach. But, the power of the AI tutor at Alpha School comes from its ability to adapt to each student’s needs.
Unlike traditional classrooms, where teachers must pace lessons for an entire group, the AI tutor adjusts difficulty, provides targeted help, and moves at the perfect speed for each individual.
Imagine a student struggling with fractions. The AI tutor might detect the specific misconception, provide extra examples, and offer practice problems at just the right difficulty level. Another student who quickly masters fractions can move ahead without waiting for his classmates.
This personalized approach eliminates the frustration of moving too slowly or too quickly through the material – a common issue in traditional education that the Texas school has solved through AI innovation. This dynamic adjustment means students learn exactly what they need, when they need it.
Benefits of the AI Tutor Approach for Students
At Alpha School, students spend two hours a day using the AI tutor for academic subjects. The AI tutor at Alpha School doesn’t just help students learn faster – it frees up time for meaningful projects.
After completing their three-hour academic block, students dive into building real-world skills. They focus on skills like public speaking, financial literacy, and teamwork. This unique structure not only improves test scores but also prepares students for real-world challenges.
Elle Kristine, a junior at Alpha School, has noticed a huge difference compared to traditional schooling. While her friends in conventional schools are swamped with homework, Elle and her classmates have more time to work on passion projects.
She’s currently developing an AI-powered dating coach for teenagers, something most 16-year-olds wouldn’t have time for in a regular school.
The Numbers Speak for Themselves
The impact of the AI tutor is undeniable. Alpha School students are now ranking in the top 2% nationally on standardized tests. That’s not just luck; it’s the power of personalized, AI-driven education. By focusing only on what each student truly needs to learn, the AI system eliminates wasted time and maximizes efficiency.
Are AI Tutors Replacing Teachers?
At Alpha School, AI isn’t replacing teachers; it’s rather transforming their role. The AI tutor handles personalized academic content delivery, freeing teachers to focus on what humans do best: providing emotional support, motivation, and hands-on guidance.
Teachers can spend their time hands-on with students and provide motivational and emotional support. This partnership between AI tutors and human teachers creates a more complete educational experience.
AI in Education and Learning
Alpha School is proving that AI in education and learning is more than just a trend; it’s the future. With AI-powered tutoring, schools can offer personalized lessons, reduce study time, and still improve academic performance. Alpha School isn’t stopping in Texas.
With their success, they’re expanding to other states, bringing their AI tutor-powered learning model to more students. Parents are excited about the possibility of giving their children a more personalized, efficient, and stress-free education.
The Future of AI Tutors in Education
Alpha School’s success with AI tutors opens exciting possibilities for education nationwide. As AI technology continues improving, these systems will become even more effective at personalizing learning experiences.
The Texas school model might be adapted for different educational settings, potentially bringing similar benefits to students in public schools, homeschool environments, and learning centres. The core principle of using AI to personalize instruction while freeing human teachers for mentorship could transform how we think about education.
Alpha School’s expansion suggests growing recognition that education needs to evolve – and AI tutors may be a key part of that evolution.
Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!
TextureFlow, The Ultimate ComfyUI Workflow for Stunning AI Texture Morphing
Have you ever wanted to transform your static images into mesmerizing animated textures? Or maybe you’ve designed a logo that needs to come alive in your videos? TextureFlow might be exactly what you’re looking for! The team behind eden.art created this incredible AI animation tool and it gives you amazing control over both shape and texture to create eye-catching animations. The best part? It’s completely free and open source!
TextureFlow is a powerful ComfyUI workflow that lets you combine any texture with any shape to create stunning morphing animations. It works without requiring any text prompts – just images in, animations out! The tool uses advanced AI models, including AnimateDiff, ControlNet, Stable Diffusion, and IP-adapter, to generate fluid, seamless animations from your static images. Unlike other AI animation tools, TextureFlow gives you precise control over both the textures and shapes in your animations.
TextureFlow Demo Video
How TextureFlow Works
At its core, TextureFlow uses your input images to drive the visual content of animations. For those familiar with AI image generation, it combines IP-adapter models with AnimateDiff video models – but don’t worry if that sounds complicated! The workflow is surprisingly simple to use:
Input one or more “style” images that define the textures
Choose a mapping mode to determine how these textures move
Optionally add a shape input to control the form of your animation
Adjust settings to fine-tune the results
Generate your animation
What makes TextureFlow special is that it doesn’t require any text prompts or special tricks. The entire process is driven by the images you provide, making it accessible even if you’re not an AI expert.
Example Animations Produced by TextureFlow
If you want to check out more, eden.art has created a collection of TextureFlow renders that you can browse. Each example includes the settings used to create it, and you can use them as presets for your own projects.
TextureFlow’s Powerful Shape Control
One of TextureFlow’s most impressive features is its shape control capability. While the animations won’t exactly reproduce your style images (they’re more like “artistic content drivers”), this actually allows for greater creative control.
Here’s how to use shape control:
Open TextureFlow settings
Add a shape input (draw one, upload an image, or upload a video)
Adjust the control strength slider to determine how strongly the shape appears
Choose style images that complement your shape
With this feature, you can create animations that maintain a specific form while displaying dynamic textures. For example, you could animate your company logo with swirling, colorful patterns while ensuring the logo remains clearly visible throughout.
Getting Started with TextureFlow
There are two main ways to use TextureFlow:
1. Online
Visit eden.art, sign up and use the TextureFlow tool directly on their website. Upon sign-up, you will be gifted with 20 free credits. You can buy more credits to start generating animations using TextureFlow
2. Locally (ComfyUI)
If you have your own GPU and know how to run ComfyUI, you can download the TextureFlow workflow and run it completely free on your own computer.
The basic process is incredibly simple. Just upload a style image, hit create, and watch as TextureFlow transforms it into a flowing animation. You can upload multiple style images, and TextureFlow will smoothly morph between them in the final animation.
Setting Up TextureFlow in ComfyUI: Step-by-Step Guide
If you want to run TextureFlow on your own computer using ComfyUI, here’s how to do it:
Step 1: Install ComfyUI
Make sure you have a compatible GPU (NVIDIA cards work best). Moreover, install Python on your computer if you don’t have it already. Download ComfyUI from GitHub: https://github.com/comfyanonymous/ComfyUI. Follow the installation instructions in the README file to get it running.
Step 2: Install Required Models
TextureFlow needs specific models to work properly:
Download the AnimateDiff model and place it in the ComfyUI models folder
Get the necessary ControlNet models
Install IP-adapter models for texture processing
Make sure you have a Stable Diffusion checkpoint (like SD 1.5)
Step 3: Download TextureFlow Workflow
Download the TextureFlow.JSON file. Save it somewhere you can easily find it.
Step 4: Load TextureFlow in ComfyUI
Start ComfyUI by running the appropriate script for your system. Once the interface loads in your browser, click on “Load” in the top menu. Navigate to where you saved TextureFlow.JSON and select it. The entire workflow will appear on your canvas.
Step 5: Configure Your Inputs
Find the image loader nodes and click on them to load your style images. If using shape control, find the shape input node and load your shape image or video. Adjust the settings nodes to customize your animation:
Motion mode
Control strength
Resolution
Generation steps
Motion strength
Boundary softness
Step 6: Generate Your Animation
Make sure all connections in the workflow are intact. Click the “Queue Prompt” button to start processing. Then, wait for the animation to render (this can take time, depending on your GPU). The final animation will appear in the output panel.
Step 7: Save Your Results
When the animation is complete, right-click on the output and select “Save”. Choose where to save your animation file. For future use, you can also save your modified workflow using the “Save” option in the top menu.
Troubleshooting Tips
If you get error messages about missing models, make sure all required models are properly installed
Check all connections in the workflow if you’re getting unexpected results
For memory issues, try reducing the resolution or number of generation steps
Join the ComfyUI community forums if you need more specific help
With these steps, you should be able to run TextureFlow on your own computer and start creating amazing AI animations!
Creating Animated QR Codes with TextureFlow
One of the coolest applications of TextureFlow is making animated QR codes that still work when scanned. Here’s how:
Upload your QR code as the shape input
Set the shape guidance type to “luminance” (which works best for QR patterns)
Add style images that will become the textures in your animation
Adjust the control strength to ensure the QR code remains scannable
Use the “activate upscale” toggle to test before creating your final version
The result is a dynamic, eye-catching QR code that draws attention while still functioning perfectly when scanned with a phone.
Taking TextureFlow to the Next Level
TextureFlow gets even more powerful when you use videos or GIFs as shape inputs. This allows you to create complex animations where both the shape and texture evolve over time.
To try this:
Find or create a short video clip or GIF
Upload it as your shape input in TextureFlow
Add complementary style images
Adjust settings to balance shape control and texture expression
Generate your animation
This technique can create mesmerizing results that would be nearly impossible to achieve with traditional animation methods.
Advanced TextureFlow Settings
To get the most out of TextureFlow, try adjusting these advanced settings:
1. AI Strength
Controls how much denoising is applied to the shape input. Typically kept at 1, but reducing to 0.8-0.9 can help preserve some aspects of the input shape.
2. Fit Strategy
Determines how your shape input maps to the output aspect ratio. Options include stretch, fill, crop, and pad.
3. Input Resolution
Even when using the upscale feature, changing the initial rendering resolution affects the complexity of patterns in your animation. Lower resolutions create simpler, more elegant patterns, while higher resolutions add more detail and visual complexity.
4. Generation Steps
Controls how much processing is used. Higher values take longer but can produce better results. Start with 5-8 for testing, then increase for your final version.
5. Motion Strength
Adjusts how dynamic the animation appears. Lower values create smoother, steadier animations, while higher values add more movement and energy.
6. Boundary Softness
Determines how sharp or gradual the transitions are between different texture regions in your animation.
Best Use Cases for TextureFlow
TextureFlow excels at creating abstract, artistic morphing patterns and animations. This makes it perfect for creating:
Abstract VJ loops for projection mapping
Animated logos for your brand
Dynamic QR codes that still work when scanned
Mesmerizing animations mapped to specific shapes like buildings or natural formations
Creative social media content that stands out
Experience the Magic of TextureFlow Today
TextureFlow represents an exciting new frontier in AI-powered animation, giving creative professionals and hobbyists alike the ability to create stunning, professional-quality animations with minimal effort.
Whether you’re a digital artist looking to expand your toolkit, a marketer seeking eye-catching visual content, or just someone who loves creating cool animations, TextureFlow offers an accessible yet powerful way to bring your static images to life.
Start experimenting with TextureFlow today and discover the endless creative possibilities this innovative ComfyUI workflow has to offer!
Research is my hobby and I love to learn new skills. I make sure that every piece of content that you read on this blog is easy to understand and fact checked!
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