Summary of Motion Generation From Fine-grained Textual Descriptions, by Kunhang Li and Yansong Feng
Motion Generation from Fine-grained Textual Descriptions
by Kunhang Li, Yansong Feng
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel task called text2motion, which aims to generate human motion sequences from textual descriptions. The model explores various mappings between natural language instructions and human body movements. Unlike existing works that focus on coarse-grained motion descriptions, this study delves into fine-grained descriptions specifying movements of relevant body parts. To address the limitation of models trained with coarse-grained texts, the authors create a large-scale language-motion dataset, FineHumanML3D, using GPT-3.5-turbo and step-by-step instructions with pseudo-code compulsory checks. They also design a new text2motion model, FineMotionDiffuse, which leverages fine-grained textual information. The quantitative evaluation shows that FineMotionDiffuse trained on FineHumanML3D improves FID by 0.38 compared to competitive baselines. Qualitative evaluations and case studies demonstrate the superiority of the proposed model in generating spatially or chronologically composite motions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make computers understand human language descriptions of movements, like “A man squats.” They want to teach a computer to generate different movement sequences based on these descriptions. This is important because most current models can only understand simple movements and not more complex ones. To solve this problem, the researchers created a big dataset with lots of examples of fine-grained motion descriptions. Then, they designed a new model that uses this data to learn how to create human-like movements from text. The results show that their model is much better at generating movement sequences than other models. |
Keywords
» Artificial intelligence » Gpt
