Summary of Divdiff: a Conditional Diffusion Model For Diverse Human Motion Prediction, by Hua Yu et al.
DivDiff: A Conditional Diffusion Model for Diverse Human Motion Prediction
by Hua Yu, Yaqing Hou, Wenbin Pei, Qiang Zhang
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel conditional diffusion-based generative model, called DivDiff, to predict diverse and realistic human motions given an observed motion sequence. The authors address the limitations of current solutions by introducing denoising diffusion models (DDPM) directly into diverse human motion prediction (HMP). They also design a diversified reinforcement sampling function (DRSF) to enforce human skeletal constraints on the predicted motions. The model is evaluated on two widely-used datasets, Human3.6M and HumanEva-I, demonstrating competitive performance on both diversity and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict what someone will do next based on how they’re moving now. That’s what this paper is about! It wants to make better predictions by using a special kind of computer model called a “denoising diffusion model”. But sometimes these models can get too noisy, making the predictions unrealistic. To fix this, the researchers created a new model that uses a different type of transform and a sampling function to keep the predicted movements realistic and following human skeletal constraints. They tested their model on two big datasets and got good results! |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Generative model
