Summary of Enhancing Motion Variation in Text-to-motion Models Via Pose and Video Conditioned Editing, by Clayton Leite and Yu Xiao
Enhancing Motion Variation in Text-to-Motion Models via Pose and Video Conditioned Editing
by Clayton Leite, Yu Xiao
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This research paper proposes a novel method for generating human poses from textual descriptions. Current text-to-motion models are limited by their training data, which doesn’t include complex motions like kicking a football with the instep of the foot. To overcome this limitation, the authors use short video clips or images as conditions to modify existing basic motions. The model’s understanding of a kick serves as the prior, while the video or image of a football kick acts as the posterior. This approach enables the generation of desired motions not present in the training set, such as kicking a football with the instep of the foot. The authors conduct a user study with 26 participants and demonstrate that their method produces unseen motions with realism comparable to commonly represented motions in text-motion datasets like HumanML3D. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about improving computers’ ability to understand and generate human movements from written descriptions. Currently, these models can only create simple movements, but not complex ones like kicking a football. The authors came up with a new way to make these models more realistic by using short videos or images as hints to modify the movements they create. This allows them to generate new movements that aren’t even in their training data. The researchers tested this method and found that it can create realistic movements, including ones that are not commonly seen. |
