ONNX
GGUF
English
text-to-motion
motion-generation
animation
3d-animation
character-animation
skeletal-animation
game-development
flow-matching
cpu-inference
qtmesheditor
feature-extraction
Instructions to use fernandotonon/QtMeshEditor-t2m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use fernandotonon/QtMeshEditor-t2m with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf fernandotonon/QtMeshEditor-t2m:Q8_0 --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf fernandotonon/QtMeshEditor-t2m:Q8_0 --embeddings
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible embeddings server: ./build/bin/llama serve -hf fernandotonon/QtMeshEditor-t2m:Q8_0 --embeddings
- Llama new
- LM Studio
- Jan
- Ollama
How to use fernandotonon/QtMeshEditor-t2m with Ollama:
ollama run hf.co/fernandotonon/QtMeshEditor-t2m:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use fernandotonon/QtMeshEditor-t2m with Docker Model Runner:
docker model run hf.co/fernandotonon/QtMeshEditor-t2m:Q8_0
- Lemonade
How to use fernandotonon/QtMeshEditor-t2m with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull fernandotonon/QtMeshEditor-t2m:Q8_0
Run and chat with the model
lemonade run user.QtMeshEditor-t2m-Q8_0
List all available models
lemonade list
- Atomic Chat
docs: refer to the validation clip generically as a reference walk
Browse files
README.md
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@@ -27,7 +27,7 @@ Generative text→skeletal-animation model shipped with
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Earlier versions trained on the **CMU MoCap** corpus and inherited a facing
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problem: generated walks moved convincingly but oriented backwards. Measuring
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against a real
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away from it, *further than a real punch, dance or jump*, and the real-vs-real
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floor is 0.437. The corpus never carried the correct convention, so the model
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could not learn it. v6.x models scored 1.89 while their own training data
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@@ -46,7 +46,7 @@ new loss:
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ankle. The v7.x walk bent the knee 25° and the ankle 31° (real: 38°/10°),
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which renders as a near-straight knee with a false second joint below it.
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### Measured against a real
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| | v7.x | **v8.0** | real |
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Earlier versions trained on the **CMU MoCap** corpus and inherited a facing
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problem: generated walks moved convincingly but oriented backwards. Measuring
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against a real reference walk showed why — CMU "walk" windows sit 1.73–2.07 rad
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away from it, *further than a real punch, dance or jump*, and the real-vs-real
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floor is 0.437. The corpus never carried the correct convention, so the model
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could not learn it. v6.x models scored 1.89 while their own training data
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ankle. The v7.x walk bent the knee 25° and the ankle 31° (real: 38°/10°),
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which renders as a near-straight knee with a false second joint below it.
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### Measured against a real reference walk
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| | v7.x | **v8.0** | real |
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