Summary of V-vipe: Variational View Invariant Pose Embedding, by Mara Levy and Abhinav Shrivastava
V-VIPE: Variational View Invariant Pose Embedding
by Mara Levy, Abhinav Shrivastava
First submitted to arxiv on: 9 Jul 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 paper presents a novel approach to estimating 3D human pose from 2D images. The authors propose separating the problem into two steps: first, finding an embedding that represents 3D poses in canonical coordinate space using a variational autoencoder (VAE); and second, encoding 2D and 3D poses with this embedding for downstream tasks like retrieval and classification. This embedding, called V-VIPE, allows for diverse applications such as estimating 3D poses from the embeddings or generating unseen 3D poses. The authors claim that their representation is unique in offering this versatility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to recognize people’s movements just by looking at pictures of them. Usually, we try to guess what a person is doing based on what they look like in a photo. But sometimes it’s hard because the picture doesn’t show everything. This makes it difficult to compare how someone was moving earlier with how they are moving now. The authors found a way to make this easier by breaking the problem into two parts. First, they create a special code that represents what people are doing in a certain position. Then, they use this code to look at 2D pictures and 3D movements. This helps us understand people’s actions better. |
Keywords
» Artificial intelligence » Classification » Embedding » Variational autoencoder
