Summary of Generation Of Complex 3d Human Motion by Temporal and Spatial Composition Of Diffusion Models, By Lorenzo Mandelli et al.
Generation of Complex 3D Human Motion by Temporal and Spatial Composition of Diffusion Models
by Lorenzo Mandelli, Stefano Berretti
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper tackles the challenge of generating realistic 3D human motions for action classes that were never seen during the training phase. The approach involves decomposing complex actions into simpler movements observed during training by leveraging GPTs models’ knowledge of human motion. These simpler movements are then combined using diffusion model properties to synthesize a realistic animation representing the complex input action. This method operates during inference and can be integrated with any pre-trained diffusion model, enabling synthesis of unseen motion classes. The paper evaluates its performance on two benchmark human motion datasets by dividing them into basic and complex actions, comparing it against state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create realistic 3D human motions for actions we haven’t seen before. It breaks down complex movements into simpler ones we have seen, using special computer models called GPTs. Then, it combines these simple movements to make a new animation that looks like the original action. This method works while predicting and can be used with any existing computer model that creates animations. We test this by looking at two big datasets of human motion and comparing our results to the best methods so far. |
Keywords
» Artificial intelligence » Diffusion model » Inference
