Summary of Towards Practical Single-shot Motion Synthesis, by Konstantinos Roditakis and Spyridon Thermos and Nikolaos Zioulis
Towards Practical Single-shot Motion Synthesis
by Konstantinos Roditakis, Spyridon Thermos, Nikolaos Zioulis
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); 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 addresses the limitations of cold-start text prompt generation by exploring unconditional synthesis from a single sample. Specifically, it focuses on accelerating the training time of Generative Adversarial Networks (GANs) for motion generation. To overcome GAN’s equilibrium collapse in mini-batch training, the authors carefully anneal loss function weights to prevent mode collapse. Additionally, they perform statistical analysis to identify correlations between training stages and enable transfer learning. The improved GAN achieves competitive quality and diversity on the Mixamo benchmark while being up to 6.8 times faster than the original GAN architecture and 1.75 times faster than a single-shot diffusion model. The paper also demonstrates the ability of the improved GAN to mix and compose motion with a single forward pass. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding new ways to create animations using computers. Currently, it’s hard to make animations because we need lots of data and computing power, and there are concerns about who owns the animation and what kind of information is included. The authors looked at an alternative method that only needs one sample to start with, which can be useful for generating new animations. They worked on speeding up a type of computer program called Generative Adversarial Networks (GANs) to generate motion. To do this, they made some adjustments to how the GAN is trained and analyzed the patterns in the data to make it work better. The result is an animation generator that can create high-quality animations quickly. |
Keywords
» Artificial intelligence » Diffusion model » Gan » Loss function » Prompt » Transfer learning
