Update README.md

README.md

@@ -199,29 +199,55 @@ The dataset is organized as:
     python humanml3d_272/prepare/download_Causal_TAE_t2m_272_ckpt.py
     ```
 
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 3. Train text to motion model:
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     > We provide scripts to train the original text to motion generation model with llama blocks, Two-Forward strategy and QK-Norm, using the motion latents encoded by the Causal TAE (trained in the first stage).
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     3.1 Get motion latents:
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+    > This step uses the pre-trained Causal TAE model to encode raw HumanML3D motion data into latent representations, saving them to humanml3d_272/t2m_latents for text-to-motion model training.
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    ```bash
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    python get_latent.py --resume-pth Causal_TAE/net_last.pth --latent_dir humanml3d_272/t2m_latents
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    ```
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     3.2 Download [sentence-T5-XXL model](https://huggingface.co/sentence-transformers/sentence-t5-xxl/tree/main) on Hugging Face:
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    ```bash
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    huggingface-cli download --resume-download sentence-transformers/sentence-t5-xxl --local-dir sentencet5-xxl/
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    ```
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     3.3 Train text to motion generation model:
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    ```bash
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    bash TRAIN_t2m.sh ${NUM_GPUS}
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    ```
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     > e.g., if you have 8 GPUs, run: bash TRAIN_t2m.sh 8
 
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     > The checkpoint will be stored at:
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     ``Experiments/t2m_model/``
 
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     > Tensorboard visualization:
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     ```bash
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     tensorboard --logdir='Experiments/t2m_model'
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     ```
 
 4. Train streaming motion generation model (MotionStreamer):