Summary of Flexible Motion In-betweening with Diffusion Models, by Setareh Cohan et al.
Flexible Motion In-betweening with Diffusion Models
by Setareh Cohan, Guy Tevet, Daniele Reda, Xue Bin Peng, Michiel van de Panne
First submitted to arxiv on: 17 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The paper explores the potential of diffusion models in generating diverse human motions guided by keyframes, a fundamental task in character animation. The authors propose Conditional Motion Diffusion In-betweening (CondMDI), a unified model that can generate precise and diverse motions conforming to user-specified spatial constraints and text conditioning. Unlike previous methods, CondMDI allows for arbitrary dense-or-sparse keyframe placement and partial keyframe constraints while generating high-quality motions that are diverse and coherent with the given keyframes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses special computer models called diffusion models to create realistic human movements between two specific points in time. These models can take direction from text prompts or user-inputted instructions. The goal is to make character animation easier by allowing for more flexible and precise control over the movement of characters. The authors test their approach on a dataset of human movements and compare it to other methods. |
Keywords
» Artificial intelligence » Diffusion
