Cool Work !
David Chen
DIsney0155
AI & ML interests
Computer Vision, Unsupervised Learning
Recent Activity
liked
a Space
1 day ago
DawnC/VividFlow
Organizations
None yet
reacted to
DawnC's
post with 🔥
1 day ago
Post
2443
VividFlow: AI Image-to-Video Generation 🎬✨
Bring your images to life with cinematic motion! VividFlow transforms any static image—portraits, artwork, products, or landscapes, into dynamic videos with professional animation quality.
The system supports both curated motion templates and custom natural language prompts, giving you complete creative freedom to describe camera movements, subject actions, and atmospheric effects in your own words.
What's Inside?
🎭 Smart Motion Templates — 8 curated categories from fashion cinematography to wildlife animations, each with tested prompts that prevent common artifacts like phantom hands in portraits
⚡ Optimized Engine — Powered by Wan2.2-I2V-A14B with Lightning LoRA distillation and FP8 quantization for memory-efficient inference
🎯 Full Creative Control — Seed-based reproducibility for consistent results, adjustable duration from half a second to five seconds, optional AI prompt expansion with Qwen2.5 for enhanced descriptions, and real-time resolution preview
Current Performance & Development Roadmap
VividFlow runs on ZeroGPU with generation taking about 3-4 minutes for 3-second videos. While I am actively optimizing the pipeline to reduce this time, the current version prioritizes output stability and quality, results are worth the wait!
Future development focuses on dedicated GPU deployment for faster processing, batch generation to create multiple variations at once, and expanding our motion template library based on what the community wants to see.
👉 Try it now: DawnC/VividFlow
If VividFlow brings motion to your creative vision, please show your support with a ❤️, your engagement influences future development priorities!
#AI #ImageToVideo #GenerativeAI #VideoGeneration #DeepLearning
Bring your images to life with cinematic motion! VividFlow transforms any static image—portraits, artwork, products, or landscapes, into dynamic videos with professional animation quality.
The system supports both curated motion templates and custom natural language prompts, giving you complete creative freedom to describe camera movements, subject actions, and atmospheric effects in your own words.
What's Inside?
🎭 Smart Motion Templates — 8 curated categories from fashion cinematography to wildlife animations, each with tested prompts that prevent common artifacts like phantom hands in portraits
⚡ Optimized Engine — Powered by Wan2.2-I2V-A14B with Lightning LoRA distillation and FP8 quantization for memory-efficient inference
🎯 Full Creative Control — Seed-based reproducibility for consistent results, adjustable duration from half a second to five seconds, optional AI prompt expansion with Qwen2.5 for enhanced descriptions, and real-time resolution preview
Current Performance & Development Roadmap
VividFlow runs on ZeroGPU with generation taking about 3-4 minutes for 3-second videos. While I am actively optimizing the pipeline to reduce this time, the current version prioritizes output stability and quality, results are worth the wait!
Future development focuses on dedicated GPU deployment for faster processing, batch generation to create multiple variations at once, and expanding our motion template library based on what the community wants to see.
👉 Try it now: DawnC/VividFlow
If VividFlow brings motion to your creative vision, please show your support with a ❤️, your engagement influences future development priorities!
#AI #ImageToVideo #GenerativeAI #VideoGeneration #DeepLearning
-
- 4 replies
liked
a
Space
1 day ago
upvoted
an
article
6 months ago
Article
Kimina-Prover: Applying Test-time RL Search on Large Formal Reasoning Models
•
53
reacted to
DawnC's
post with 🔥
7 months ago
Post
3268
VisionScout Major Update: Enhanced Precision Through Multi-Modal AI Integration
I'm excited to share significant improvements to VisionScout that substantially enhance accuracy and analytical capabilities.
⭐️ Key Enhancements
- CLIP Zero-Shot Landmark Detection: The system now identifies famous landmarks and architectural features without requiring specific training data, expanding scene understanding beyond generic object detection.
- Places365 Environmental Classification: Integration of MIT's Places365 model provides robust scene baseline classification across 365 categories, significantly improving lighting analysis accuracy and overall scene identification precision.
- Enhanced Multi-Modal Fusion: Advanced algorithms now dynamically combine insights from YOLOv8, CLIP, and Places365 to optimize accuracy across diverse scenarios.
- Refined LLM Narratives: Llama 3.2 integration continues to transform analytical data into fluent, contextually rich descriptions while maintaining strict factual accuracy.
🎯 Future Development Focus
Accuracy remains the primary development priority, with ongoing enhancements to multi-modal fusion capabilities. Future work will advance video analysis beyond current object tracking foundations to include comprehensive temporal scene understanding and dynamic narrative generation.
Try it out 👉 DawnC/VisionScout
If you find this update valuable, a Like❤️ or comment means a lot!
#LLM #ComputerVision #MachineLearning #MultiModel #TechForLife
I'm excited to share significant improvements to VisionScout that substantially enhance accuracy and analytical capabilities.
⭐️ Key Enhancements
- CLIP Zero-Shot Landmark Detection: The system now identifies famous landmarks and architectural features without requiring specific training data, expanding scene understanding beyond generic object detection.
- Places365 Environmental Classification: Integration of MIT's Places365 model provides robust scene baseline classification across 365 categories, significantly improving lighting analysis accuracy and overall scene identification precision.
- Enhanced Multi-Modal Fusion: Advanced algorithms now dynamically combine insights from YOLOv8, CLIP, and Places365 to optimize accuracy across diverse scenarios.
- Refined LLM Narratives: Llama 3.2 integration continues to transform analytical data into fluent, contextually rich descriptions while maintaining strict factual accuracy.
🎯 Future Development Focus
Accuracy remains the primary development priority, with ongoing enhancements to multi-modal fusion capabilities. Future work will advance video analysis beyond current object tracking foundations to include comprehensive temporal scene understanding and dynamic narrative generation.
Try it out 👉 DawnC/VisionScout
If you find this update valuable, a Like❤️ or comment means a lot!
#LLM #ComputerVision #MachineLearning #MultiModel #TechForLife
reacted to
DawnC's
post with 🤗
8 months ago
Post
2963
VisionScout — Now with Scene Understanding! 🚀
I'm excited to share a major update to VisionScout, my interactive vision tool that combines powerful object detection with emerging scene understanding capabilities! 👀🔍
What can VisionScout do today?
🖼️ Upload any image and detect 80 object types using YOLOv8.
🔄 Instantly switch between Nano, Medium, and XLarge models depending on speed vs. accuracy needs.
🎯 Filter specific classes (people, vehicles, animals, etc.) to focus only on what matters to you.
📊 View detailed statistics on detected objects, confidence levels, and spatial distribution.
⭐️ NEW: Scene understanding layer now added!
- Automatically interprets the scene based on detected objects.
- Uses a combination of rule-based reasoning and CLIP-powered semantic validation.
- Outputs descriptions, possible activities, and even safety concerns.
What’s coming next?
🔎 Expanding YOLO’s object categories.
🎥 Adding video processing and multi-frame object tracking.
⚡ Faster real-time performance.
📱 Improved mobile responsiveness.
My goal:
To make advanced vision tools accessible to everyone, from beginners to experts , while continuing to push for more accurate and meaningful scene interpretation.
Try it yourself! 🖼️
👉 DawnC/VisionScout
If you enjoy VisionScout, feel free to give the project a ❤️, it really helps and keeps me motivated to keep building and improving!
Stay tuned for more updates!
#ComputerVision #ObjectDetection #YOLO #SceneUnderstanding #MachineLearning #TechForLife
I'm excited to share a major update to VisionScout, my interactive vision tool that combines powerful object detection with emerging scene understanding capabilities! 👀🔍
What can VisionScout do today?
🖼️ Upload any image and detect 80 object types using YOLOv8.
🔄 Instantly switch between Nano, Medium, and XLarge models depending on speed vs. accuracy needs.
🎯 Filter specific classes (people, vehicles, animals, etc.) to focus only on what matters to you.
📊 View detailed statistics on detected objects, confidence levels, and spatial distribution.
⭐️ NEW: Scene understanding layer now added!
- Automatically interprets the scene based on detected objects.
- Uses a combination of rule-based reasoning and CLIP-powered semantic validation.
- Outputs descriptions, possible activities, and even safety concerns.
What’s coming next?
🔎 Expanding YOLO’s object categories.
🎥 Adding video processing and multi-frame object tracking.
⚡ Faster real-time performance.
📱 Improved mobile responsiveness.
My goal:
To make advanced vision tools accessible to everyone, from beginners to experts , while continuing to push for more accurate and meaningful scene interpretation.
Try it yourself! 🖼️
👉 DawnC/VisionScout
If you enjoy VisionScout, feel free to give the project a ❤️, it really helps and keeps me motivated to keep building and improving!
Stay tuned for more updates!
#ComputerVision #ObjectDetection #YOLO #SceneUnderstanding #MachineLearning #TechForLife
liked
a
Space
8 months ago
liked
a
Space
9 months ago