An AI-powered system recommends and generates poses based on user and environmental constraints. The system categorizes suitable environments and outfits by analyzing image backgrounds or user-provided prompts. The system detects key points on the human body (joints, limbs, head, etc.), ensuring accurate understanding of the user's body positioning. AI models generate recommended poses, facilitating better user understanding. Advanced Generative AI Models (e.g., Stable Diffusion Models) transform user-input images and prompts into recommended poses. The system provides personalized pose suggestions tailored to user profile details such as preferences, characteristics, and goals (yoga, aesthetics, fitness, etc.).
PoseGen is a highly innovative solution in personalized pose recommendations, offering users a unique and advanced experience in a rapidly evolving field.
- Deep Learning Integration
- Dual Input Modalities
- Environment and Outfit Classification
- Key Point Detection
- Advanced Generative AI Models (Stable Diffusion Models)
- Personalized Pose Suggestions
- Pose suggestions based on user profile details, preferences, characteristics, and goals reflects
- The accurate detection of key points on the human body, such as joints and limbs
- PoseGen's choice to employ advanced models like Stable Diffusion Models for pose generation
- Before : It show the model layer without the controlnet attentation
- After : The Controlnet layer used to shape the output result from the stable difussion model
- HTML | CSS
- NodeJS
- ExpressJS
- EJS Engine
- Python
- Flask
- Tensorflow
- Deep Learning (CNN)
- Python
- Pytorch
- Stable Diffiussion (Controlnet)
