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MotionGen\u5de5\u5177\u57fa\u4e8e\u5143\u8c61\u81ea\u4e3b\u7814\u53d1\u7684MotionGenGPT\u7b97\u6cd5\uff0c\u5c06\u7269\u7406\u4eff\u771f\u3001\u6a21\u4eff\u5b66\u4e60\u3001\u5f3a\u5316\u5b66\u4e60\u3001\u77e2\u91cf\u91cf\u5316\u53d8\u5206\u81ea\u7f16\u7801\u5668\u3001Transformer\u6a21\u578b\u7b49\u591a\u79cd\u590d\u6742\u7b97\u6cd5\u8fdb\u884c\u521b\u65b0\u6027\u878d\u5408\uff0c\u65e0\u9700\u4eba\u5de5\u8bbe\u5b9a\u6216\u8c03\u6574\u53c2\u6570\uff0c\u53ef\u76f4\u63a5\u751f\u6210\u903c\u771f\u6d41\u7545\u7684\u590d\u67423D\u52a8\u4f5c\uff0c\u5e76\u53ef\u5e94\u7528\u5230\u4efb\u610f\u89d2\u8272\u7684\u9aa8\u67b6\u9a71\u52a8\u3002<\/p>\n

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VQ-VAE\uff1a\u63d0\u53d6\u7279\u5f81\u5e76\u590d\u7528<\/strong><\/p>\n

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Transformer\uff1a\u6587\u672c\u4e0e\u52a8\u4f5c\u7684\u9ad8\u6548\u8fde\u63a5<\/strong><\/p>\n

\u5728\u6587\u672c\u751f\u6210\u52a8\u4f5c\u7684\u4efb\u52a1\u4e2d\uff0cTransformer \u7ed3\u6784\u53d1\u6325\u4e86\u5173\u952e\u4f5c\u7528\u3002\u53d7\u5e8f\u5217\u6a21\u578b\u8fd1\u4e24\u5e74\u53d1\u5c55\u7684\u542f\u53d1\uff0c\u6211\u4eec\u901a\u8fc7 Transformer \u7684\u6ce8\u610f\u529b\u673a\u5236[9]<\/sup>\uff0c\u5728\u6587\u672c\u4e0e\u52a8\u4f5c\u8868\u5f81\u4e4b\u95f4\u5efa\u7acb\u4e86\u590d\u6742\u800c\u7cbe\u51c6\u7684\u8054\u7cfb\u3002\u901a\u8fc7\u53cc Transformer \u8bbe\u8ba1\uff0c\u6a21\u578b\u4e0d\u4ec5\u751f\u6210\u4e86\u57fa\u7840\u52a8\u4f5c\u7f16\u7801\uff0c\u8fd8\u8fdb\u4e00\u6b65\u7ec6\u5316\u4e86\u8fd9\u4e9b\u7f16\u7801\uff0c\u6355\u6349\u5230\u8fd0\u52a8\u7684\u5fae\u5999\u7ec6\u8282\u3002\u8fd9\u79cd\u591a\u5c42\u6b21\u7f16\u7801\u65b9\u5f0f\u4f7f\u751f\u6210\u7684\u52a8\u4f5c\u65e2\u7b26\u5408\u7269\u7406\u89c4\u5f8b\uff0c\u53c8\u5c55\u73b0\u51fa\u81ea\u7136\u7684\u6d41\u52a8\u6027\u548c\u591a\u6837\u6027\uff0c\u6781\u5927\u63d0\u5347\u4e86\u6587\u672c\u9a71\u52a8\u52a8\u4f5c\u751f\u6210\u7684\u8868\u73b0\u529b\u3002\u8be5\u8bbe\u8ba1\u4f7f MotionGenGPT \u80fd\u591f\u4ece\u6587\u672c\u4e2d\u751f\u6210\u591a\u6837\u4e14\u9ad8\u5ea6\u903c\u771f\u7684\u8fd0\u52a8\u5e8f\u5217\uff0c\u5b9e\u73b0\u4e86\u81ea\u7136\u8bed\u8a00\u4e0e\u8fd0\u52a8\u63a7\u5236\u7684\u65e0\u7f1d\u8fde\u63a5\u3002<\/p>\n

<\/a><\/p>\n

MotionGenGPT\u7b97\u6cd5\u5206\u4e3a\u4e24\u5927\u6846\u67b6\uff1a<\/strong><\/p>\n

\u7b2c\u4e00\u90e8\u5206\u662f\u5b66\u4e60\u7269\u7406\u6b63\u786e\u7684\u52a8\u4f5c\u8868\u5f81\uff08motion embeddings\uff09\u3002\u7814\u53d1\u56e2\u961f\u57fa\u4e8e\u7269\u7406\u6a21\u62df\u7684\u8fd0\u52a8\u63a7\u5236\uff0c\u91c7\u7528\u6b8b\u5dee\u77e2\u91cf\u91cf\u5316\u53d8\u5206\u81ea\u7f16\u7801\u5668\uff08Residual VQ-VAE\uff09\u7ed3\u5408\u5f3a\u5316\u5b66\u4e60\uff0c\u4ece\u5927\u91cf\u975e\u7ed3\u6784\u5316\u7684\u4eba\u7c7b\u8fd0\u52a8\u6570\u636e\u96c6\u4e2d\u5b66\u4e60\u52a8\u4f5c\u8868\u5f81\uff08motion embeddings\uff09\u3002\u901a\u8fc7\u5f15\u5165\u79bb\u6563\u52a8\u4f5c\u8868\u5f81\uff0c\u6709\u6548\u6355\u83b7\u591a\u6837\u5316\u7684\u8fd0\u52a8\u6280\u80fd\uff0c\u4e3a\u540e\u7eed\u6587\u672c\u7f16\u7801\u5668\u7684\u63a5\u5165\u63d0\u4f9b\u4e86\u7a33\u5065\u7684\u5148\u9a8c\u5206\u5e03\u63a5\u53e3\u3002<\/p>\n

<\/a><\/p>\n

\u7b2c\u4e8c\u90e8\u5206\u662f\u5bf9\u4e8e\u751f\u6210\u6a21\u578b\u7684\u5e94\u7528\uff0c\u7b97\u6cd5\u6846\u67b6\u7528\u81ea\u6ce8\u610f\u529b\u673a\u5236\uff08Self-attention mechanism\uff09\u8bad\u7ec3\u4e86\u4e24\u4e2a Transformer \u6a21\u578b\u3002\u7b2c\u4e00\u4e2a\u6a21\u578b\u662f\u63a9\u7801 Transformer\uff08Masked Transformer\uff09\uff0c\u8d1f\u8d23\u4ece\u6587\u672c\u7f16\u7801\u63a8\u7406\u51fa\u57fa\u5c42\uff08base layer\uff09\u52a8\u4f5c\u7f16\u7801\u3002\u7b2c\u4e8c\u4e2a\u6a21\u578b\u662f\u6b8b\u5dee Transformer\uff08Residual Transformer\uff09\uff0c\u57fa\u4e8e\u57fa\u5c42\u52a8\u4f5c\u7f16\u7801\u751f\u6210\u66f4\u7cbe\u7ec6\u7684\u6b8b\u5dee\u77e2\u91cf\u5316\u52a8\u4f5c\u7f16\u7801\u3002<\/p>\n

\u8fd9\u4e00\u8bbe\u8ba1\u63d0\u5347\u4e86\u751f\u6210\u52a8\u4f5c\u7684\u7ec6\u8282\u8868\u73b0\u529b\u548c\u4e0e\u6587\u672c\u6307\u4ee4\u7684\u5339\u914d\u5ea6\uff0c\u4e3a\u6587\u672c\u751f\u6210\u52a8\u4f5c\u4efb\u52a1\u63d0\u4f9b\u4e86\u652f\u6301\u3002\u00a0<\/p>\n

<\/a><\/p>\n

\u5f15\u7528<\/strong><\/p>\n

1. \u00a0Levi Fussell, Kevin Bergamin, and Daniel Holden. 2021. SuperTrack: Motion Tracking for Physically Simulated Characters Using Supervised Learning. ACM Transactions on Graphics 40, 6 (Dec. 2021), 197:1\u2013197:13.<\/span><\/p>\n

2. \u00a0Libin Liu, Michiel Van De Panne, and Kangkang Yin. 2016. Guided Learning of Control Graphs for Physics-Based Characters. ACM ransactions on Graphics 35, 3 (May 2016), 29:1\u201329:14.<\/span><\/p>\n

3. \u00a0Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne. 2018. DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills. ACM Transactions on Graphics 37, 4 (July 2018), 143:1\u2013143:14.<\/span><\/p>\n

4. \u00a0TingwuWang, Yunrong Guo, Maria Shugrina, and Sanja Fidler. 2020. UniCon: Universal Neural Controller For Physics-based Character Motion. CoRR abs\/2011.15119 (2020). arXiv:2011.15119.<\/span><\/p>\n

5. \u00a0Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu. 2017. Neural Discrete Representation Learning. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS\u201917). Curran Associates Inc., Red Hook, NY, USA, 6309\u20136318.<\/span><\/p>\n

6. \u00a0Heyuan Yao,\u00a0Zhenhua Song,\u00a0Yuyang Zhou,\u00a0Tenglong Ao,\u00a0Baoquan Chen,\u00a0Libin Liu. MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations. arXiv preprint arXiv:2310.10198, 2023.<\/span><\/p>\n

7. \u00a0Chuan Guo,\u00a0Yuxuan Mu,\u00a0Muhammad Gohar Javed,\u00a0Sen Wang, and\u00a0Li Cheng. MoMask: Generative Masked Modeling of 3D Human Motions. arXiv preprint arXiv:2312.00063, 2023.<\/span><\/p>\n

8. \u00a0Qingxu Zhu, He Zhang, Mengting Lan, and Lei Han. 2023. Neural Categorical Priors for Physics-Based Character Control. ACM Trans. Graph. 42, 6, Article 178 (December 2023), 16 pages.<\/span><\/p>\n

9. \u00a0Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS), 2017. 2, 4, 6.<\/span><\/p>\n

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