KadirErturk/image_info
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Kadir Erturk
KadirErturk
·
AI & ML interests
sensors and machine learning
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2 days ago
marduk-ra/F5-TTS-Turkish:Any plan to change licensing to MIT or Apache 2.0?
updated
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19 days ago
KadirErturk/jenny-tts-tags-6h-v1
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singhsidhukuldeep's
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about 1 month ago
Good folks at @nvidia and @Tsinghua_Uni have released LLAMA-MESH - A Revolutionary Approach to 3D Content Generation!
This innovative framework enables the direct generation of 3D meshes from natural language prompts while maintaining strong language capabilities.
Here is the Architecture & Implementation!
>> Core Components
Model Foundation
- If you haven't guessed it yet, it's built on the LLaMA-3.1-8B-Instruct base model
- Maintains original language capabilities while adding 3D generation
- Context length is set to 8,000 tokens
3D Representation Strategy
- Uses the OBJ file format for mesh representation
- Quantizes vertex coordinates into 64 discrete bins per axis
- Sorts vertices by z-y-x coordinates, from lowest to highest
- Sorts faces by the lowest vertex indices for consistency
Data Processing Pipeline
- Filters meshes to a maximum of 500 faces for computational efficiency
- Applies random rotations (0°, 90°, 180°, 270°) for data augmentation
- Generates ~125k mesh variations from 31k base meshes
- Uses Cap3D-generated captions for text descriptions
>> Training Framework
Dataset Composition
- 40% Mesh Generation tasks
- 20% Mesh Understanding tasks
- 40% General Conversation (UltraChat dataset)
- 8x training turns for generation, 4x for understanding
Training Configuration
- Deployed on 32 A100 GPUs (for Nvidia, this is literally in-house)
- 21,000 training iterations
- Global batch size: 128
- AdamW optimizer with a 1e-5 learning rate
- 30-step warmup with cosine scheduling
- Total training time: approximately 3 days (based on the paper)
This research opens exciting possibilities for intuitive 3D content creation through natural language interaction. The future of digital design is conversational!
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