- 🎥 Multimodal Video Captioning - Audio-Visual understanding
- 👁️ Computer Vision - 3D Reconstruction, Pose Estimation
- 🤖 Vision Transformers - Attention mechanisms for visual tasks
- 📊 Deep Learning Research - PyTorch implementations
- 🌐 Portfolio | 📧 ashokbk215@gmail.com
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07:20
(UTC +05:45) - github.com/blazewild
- https://www.asokbk.com.np/
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Highlights
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Real-Time-Motion-Transfer-to-a-3D-Avatar
Real-Time-Motion-Transfer-to-a-3D-Avatar PublicReal-time human pose detection and motion transfer to 3D avatars using MediaPipe, DNN, and Three.js — supports webcam and video inputs with custom avatar integration.
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Custom_LLM_DataGen_Template
Custom_LLM_DataGen_Template Public🔧 Modular pipeline for generating high-quality, domain-specific datasets for LLM fine-tuning — from PDFs and web scraping to synthetic Q&A generation, quality filtering, and training-ready formatting.
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Hav-Cocap
Hav-Cocap PublicHav-Cocap: Hybrid Audio-Visual Compressed Video Captioning framework. Extends CoCap with an Audio Encoder and evaluated on the AVCaps dataset.
C
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Blaze2Cap_AI_Motioner
Blaze2Cap_AI_Motioner Public3D Human Pose Estimation: BlazePose to TotalCapture Motion Dataset Pipeline with PyTorch DataLoader for motion capture research and machine learning
Python 2
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GPT_FROM_SCRATCH
GPT_FROM_SCRATCH PublicMinimal GPT implementation from scratch using PyTorch — trains a character-level transformer on the Tiny Shakespeare dataset to demonstrate core LLM concepts.
Jupyter Notebook
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MV_MAE
MV_MAE PublicMV-MAE is a hierarchical video model that leverages motion vectors and I-frames from compressed videos to efficiently learn masked motion representations for accurate UAV action recognition.
Python
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