AI-Based Football Drill Animation Transformation
Budget: ₹12,500 – ₹37,500 INR
Project Overview
We have a collection of 2D animated football drill videos, each using different visual templates/styles. We need an AI-based solution that can automatically transform these existing videos into a new, consistent animation style with a uniform background while preserving the drill movements and tactics.
Current Situation
• Input: Multiple 2D animated football drill videos
• Challenge: Each video uses a different template/style (varying animation styles, player designs, field layouts, color schemes)
• Goal: Transform ALL videos into a unified, professional animation style with a consistent background regardless of their original template
Key Requirement: Consistent Background System
IMPORTANT: We will provide a single, consistent background image (football field) that will be used across ALL output videos. The AI system must:
• Extract player movements, positions, and drill patterns from the original videos
• Overlay these animated players and drill movements ON TOP of the provided background image
• Ensure proper perspective, scaling, and positioning of players relative to the background
• Maintain spatial accuracy so drill patterns make tactical sense on the field
• Preserve the exact timing, speeds, and coordination of all movements
Project Requirements
1. AI Algorithm Development
Develop an intelligent system that can:
• Detect and extract football drill movements, player positions, and tactical patterns from existing 2D animations
• Analyze different video templates automatically (different animation styles, player designs, camera angles)
• Work universally across all template variations without manual intervention per video
• Preserve drill accuracy - maintain exact movements, timing, formations, and tactical instructions
• Composite players onto the provided background with correct perspective and positioning
2. Video Transformation Features
The AI solution must be able to:
Animation Style Transformation
• Convert existing 2D player animations into a specified target style (e.g., flat design, isometric, top-down tactical view, cartoon style)
• Maintain smooth motion and realistic football movement
• Ensure consistent player proportions and animations across all output videos
Background Integration & Compositing
• Seamlessly composite animated players onto the provided background image
• Automatically adjust player scale and perspective to match the background field
• Maintain proper depth perception and spatial relationships
• Ensure drill patterns align correctly with field markings and dimensions
Motion & Timing Preservation
• Retain exact drill timing and sequence
• Preserve player speeds, directions, and coordination
• Keep audio cues/voiceovers synchronized (if present)
3. Technical Approach
We're looking for solutions using:
• Computer Vision AI (pose estimation, object tracking, motion detection, background segmentation)
• Video-to-Video AI models (e.g., Stable Diffusion Video, ControlNet, AnimateDiff, Runway ML)
• Style transfer algorithms optimized for 2D animations
• Automated video processing pipelines that can batch-process multiple videos
• Compositing techniques to overlay animated elements on static backgrounds with proper perspective
The system should be:
• Template-agnostic: Works on any 2D football animation regardless of original style
• Scalable: Can process large batches of videos efficiently
• Configurable: Easy to adjust output style parameters (colors, player designs, animation styles)
• Consistent: Produces uniform results across all videos with the same background
Deliverables
1. Working AI Algorithm/System
• Documented codebase (Python preferred)
• Setup instructions and dependencies
• Configuration files for style customization and background integration
2. Processing Pipeline
• Batch processing capability for multiple videos
• Progress tracking and error handling
• Quality assurance checks
3. Sample Outputs
• Process 3-5 sample videos demonstrating the transformation
• Show consistency across different original templates
• Before/after comparisons
4. Documentation
• Technical documentation explaining the AI approach
• User guide for running the system
• Instructions for adjusting output styles and using custom backgrounds
5. Source Files
• Complete source code
• Trained models (if custom training is involved)
• Configuration templates
Ideal Candidate Profile
We're looking for someone with:
Required Skills:
• Strong experience with AI/ML video processing
• Expertise in computer vision (OpenCV, MediaPipe, YOLO)
• Knowledge of generative AI models for video (Stable Diffusion, ControlNet, AnimateDiff, or similar)
• Proficiency in Python and video processing libraries (MoviePy, FFmpeg, PyTorch/TensorFlow)
• Experience with style transfer or video-to-video translation
• Experience with video compositing and background replacement techniques
Preferred Skills:
• Previous work with sports video analysis
• Experience with 2D animation processing
• Knowledge of football/soccer tactics (helpful for understanding drill patterns)
• Portfolio showing similar video transformation projects
Project Scope & Timeline
• Project Type: Fixed price
• Estimated Timeline: 2 weeks
• Number of Videos: 100+
• Video Length: 10-60 seconds each
Application Requirements
When applying, please include:
6. Approach Overview: Brief explanation of how you would solve this technically (which AI models/methods)
7. Relevant Portfolio: Links to similar video processing/AI projects
8. Technical Questions:
• Which AI frameworks/models would you use?
• How would you handle template variations?
• How would you ensure accurate compositing on the provided background?
• Estimated processing time per video?
9. Timeline & Cost Estimate: Your proposed timeline and pricing structure
10. Sample Request: We may provide 1-2 sample videos for a proof-of-concept
Important Notes
• The solution must be automated - manual editing per video is not acceptable
• Quality consistency across all videos is critical
• The system should be transferable (we should be able to run it ourselves after delivery)
• Intellectual property: All code and models developed become our property upon completion
Questions? Please reach out before applying. We're happy to provide sample videos under NDA for serious candidates.
Sample video - https://drive.google.com/drive/folders/1k5sn2HIPT0PM3fJxC3hn5jj1L4oHkGI2?dmr=1&ec=wgc-drive-%5Bmodule%5D-goto
We have a collection of 2D animated football drill videos, each using different visual templates/styles. We need an AI-based solution that can automatically transform these existing videos into a new, consistent animation style with a uniform background while preserving the drill movements and tactics.
Current Situation
• Input: Multiple 2D animated football drill videos
• Challenge: Each video uses a different template/style (varying animation styles, player designs, field layouts, color schemes)
• Goal: Transform ALL videos into a unified, professional animation style with a consistent background regardless of their original template
Key Requirement: Consistent Background System
IMPORTANT: We will provide a single, consistent background image (football field) that will be used across ALL output videos. The AI system must:
• Extract player movements, positions, and drill patterns from the original videos
• Overlay these animated players and drill movements ON TOP of the provided background image
• Ensure proper perspective, scaling, and positioning of players relative to the background
• Maintain spatial accuracy so drill patterns make tactical sense on the field
• Preserve the exact timing, speeds, and coordination of all movements
Project Requirements
1. AI Algorithm Development
Develop an intelligent system that can:
• Detect and extract football drill movements, player positions, and tactical patterns from existing 2D animations
• Analyze different video templates automatically (different animation styles, player designs, camera angles)
• Work universally across all template variations without manual intervention per video
• Preserve drill accuracy - maintain exact movements, timing, formations, and tactical instructions
• Composite players onto the provided background with correct perspective and positioning
2. Video Transformation Features
The AI solution must be able to:
Animation Style Transformation
• Convert existing 2D player animations into a specified target style (e.g., flat design, isometric, top-down tactical view, cartoon style)
• Maintain smooth motion and realistic football movement
• Ensure consistent player proportions and animations across all output videos
Background Integration & Compositing
• Seamlessly composite animated players onto the provided background image
• Automatically adjust player scale and perspective to match the background field
• Maintain proper depth perception and spatial relationships
• Ensure drill patterns align correctly with field markings and dimensions
Motion & Timing Preservation
• Retain exact drill timing and sequence
• Preserve player speeds, directions, and coordination
• Keep audio cues/voiceovers synchronized (if present)
3. Technical Approach
We're looking for solutions using:
• Computer Vision AI (pose estimation, object tracking, motion detection, background segmentation)
• Video-to-Video AI models (e.g., Stable Diffusion Video, ControlNet, AnimateDiff, Runway ML)
• Style transfer algorithms optimized for 2D animations
• Automated video processing pipelines that can batch-process multiple videos
• Compositing techniques to overlay animated elements on static backgrounds with proper perspective
The system should be:
• Template-agnostic: Works on any 2D football animation regardless of original style
• Scalable: Can process large batches of videos efficiently
• Configurable: Easy to adjust output style parameters (colors, player designs, animation styles)
• Consistent: Produces uniform results across all videos with the same background
Deliverables
1. Working AI Algorithm/System
• Documented codebase (Python preferred)
• Setup instructions and dependencies
• Configuration files for style customization and background integration
2. Processing Pipeline
• Batch processing capability for multiple videos
• Progress tracking and error handling
• Quality assurance checks
3. Sample Outputs
• Process 3-5 sample videos demonstrating the transformation
• Show consistency across different original templates
• Before/after comparisons
4. Documentation
• Technical documentation explaining the AI approach
• User guide for running the system
• Instructions for adjusting output styles and using custom backgrounds
5. Source Files
• Complete source code
• Trained models (if custom training is involved)
• Configuration templates
Ideal Candidate Profile
We're looking for someone with:
Required Skills:
• Strong experience with AI/ML video processing
• Expertise in computer vision (OpenCV, MediaPipe, YOLO)
• Knowledge of generative AI models for video (Stable Diffusion, ControlNet, AnimateDiff, or similar)
• Proficiency in Python and video processing libraries (MoviePy, FFmpeg, PyTorch/TensorFlow)
• Experience with style transfer or video-to-video translation
• Experience with video compositing and background replacement techniques
Preferred Skills:
• Previous work with sports video analysis
• Experience with 2D animation processing
• Knowledge of football/soccer tactics (helpful for understanding drill patterns)
• Portfolio showing similar video transformation projects
Project Scope & Timeline
• Project Type: Fixed price
• Estimated Timeline: 2 weeks
• Number of Videos: 100+
• Video Length: 10-60 seconds each
Application Requirements
When applying, please include:
6. Approach Overview: Brief explanation of how you would solve this technically (which AI models/methods)
7. Relevant Portfolio: Links to similar video processing/AI projects
8. Technical Questions:
• Which AI frameworks/models would you use?
• How would you handle template variations?
• How would you ensure accurate compositing on the provided background?
• Estimated processing time per video?
9. Timeline & Cost Estimate: Your proposed timeline and pricing structure
10. Sample Request: We may provide 1-2 sample videos for a proof-of-concept
Important Notes
• The solution must be automated - manual editing per video is not acceptable
• Quality consistency across all videos is critical
• The system should be transferable (we should be able to run it ourselves after delivery)
• Intellectual property: All code and models developed become our property upon completion
Questions? Please reach out before applying. We're happy to provide sample videos under NDA for serious candidates.
Sample video - https://drive.google.com/drive/folders/1k5sn2HIPT0PM3fJxC3hn5jj1L4oHkGI2?dmr=1&ec=wgc-drive-%5Bmodule%5D-goto