Summary of Gst: Precise 3d Human Body From a Single Image with Gaussian Splatting Transformers, by Lorenza Prospero et al.
GST: Precise 3D Human Body from a Single Image with Gaussian Splatting Transformers
by Lorenza Prospero, Abdullah Hamdi, Joao F. Henriques, Christian Rupprecht
First submitted to arxiv on: 6 Sep 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 Reconstructing 3D human models from single images is crucial for creative industries, HCI, and healthcare. Our paper builds upon 3D Gaussian Splatting (3DGS), a scene representation comprising mixed Gaussians. Predicting these mixtures for humans is challenging due to the non-uniform density, physical constraints, and flexibility requirements. We utilize standardized human meshes like SMPL as an initial position for Gaussians and train a transformer model to predict small adjustments, Gaussian attributes, and SMPL parameters. Our approach achieves fast inference without test-time optimization or expensive diffusion models, while improving 3D pose estimation by fitting human models that account for clothing variations. We showcase our results using multi-view supervision and demonstrate the effectiveness of our method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create realistic 3D images of humans from just one regular photo! This technology has many uses, like creating characters for movies or helping doctors understand how people move in different clothes. We developed a new way to predict what these 3D models should look like based on a single image. Our method uses special computer algorithms and is really good at guessing the right shape and pose of a person, even with different outfits or accessories. This means we can create more realistic characters for movies or help doctors better understand how people move in real-life situations. |
Keywords
» Artificial intelligence » Inference » Optimization » Pose estimation » Transformer
