Summary of D-lord For Motion Stylization, by Meenakshi Gupta et al.
D-LORD for Motion Stylization
by Meenakshi Gupta, Mingyuan Lei, Tat-Jen Cham, Hwee Kuan Lee
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed D-LORD (Double Latent Optimization for Representation Disentanglement) framework is designed for motion stylization, aiming to separate class and content information from a given motion sequence using a data-driven latent optimization approach. This novel framework enables style transfer without requiring paired motion data, instead utilizing class and content labels during the latent optimization process. By disentangling the representation, D-LORD allows the transformation of one motion sequence’s style to another’s style using Adaptive Instance Normalization. The framework is designed for generalization, handling different class and content labels for various applications and generating diverse motion sequences when specific labels are provided. Experimental results demonstrate the efficacy of D-LORD on three datasets: CMU XIA for motion style transfer, MHAD, and RRIS Ability for motion retargeting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to change the style of movement in videos or animations. It’s called D-LORD, which stands for Double Latent Optimization for Representation Disentanglement. The goal is to separate what makes someone unique from the basic actions they’re doing, like walking or jumping. This helps us change one person’s movement style to another’s without needing special training data. The result is a new movement that looks like it could have come from the second person. This method works well and can be used in different situations. |
Keywords
» Artificial intelligence » Generalization » Optimization » Style transfer
