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from openai import AzureOpenAI |
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from models.motion_agent import MotionAgent |
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from models.mllm import MotionLLM |
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from options.option_llm import get_args_parser |
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from utils.motion_utils import recover_from_ric, plot_3d_motion |
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from utils.paramUtil import t2m_kinematic_chain |
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import torch |
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import os |
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from openai import OpenAI |
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def motion_agent_demo(): |
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client = OpenAI( |
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api_key="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") |
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args = get_args_parser() |
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args.save_dir = "./demo" |
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args.device = 'cuda:1' |
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motion_agent = MotionAgent(args, client) |
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motion_agent.chat() |
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def motionllm_demo(): |
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model = MotionLLM() |
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model.load_model('ckpt/motionllm.pth') |
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model.llm.eval() |
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model.llm.cuda() |
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caption = 'A man is doing cartwheels.' |
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motion = model.generate(caption) |
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motion = MotionLLM.denormalize(motion.detach().cpu().numpy()) |
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motion = recover_from_ric(torch.from_numpy(motion).float().cuda(), 22) |
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print(motion.shape) |
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plot_3d_motion(f"motionllm_demo.mp4", t2m_kinematic_chain, motion.squeeze().detach().cpu().numpy(), title=caption, fps=20, radius=4) |
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if __name__ == "__main__": |
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motion_agent_demo() |
