# 4D Human Body Correspondences from Panoramic Depth Maps

arXiv: https://arxiv.org/abs/1810.05340 · HTML: https://mlsanity.com/paper/1810.05340/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis](https://mlsanity.com/paper/2312.16812/) (2023)
- [TeTriRF: Temporal Tri-Plane Radiance Fields for Efficient Free-Viewpoint Video](https://mlsanity.com/paper/2312.06713/) (2023)
- [Segment Any 4D Gaussians](https://mlsanity.com/paper/2407.04504/) (2024)
- [Dynamic 2D Gaussians: Geometrically Accurate Radiance Fields for Dynamic Objects](https://mlsanity.com/paper/2409.14072/) (2024)
- [Modeling Ambient Scene Dynamics for Free-view Synthesis](https://mlsanity.com/paper/2406.09395/) (2024)
- [Relit-NeuLF: Efficient Relighting and Novel View Synthesis via Neural 4D Light Field](https://mlsanity.com/paper/2310.14642/) (2023)

## Abstract (arXiv)

The availability of affordable 3D full body reconstruction systems has given rise to free-viewpoint video (FVV) of human shapes. Most existing solutions produce temporally uncorrelated point clouds or meshes with unknown point/vertex correspondences. Individually compressing each frame is ineffective and still yields to ultra-large data sizes. We present an end-to-end deep learning scheme to establish dense shape correspondences and subsequently compress the data. Our approach uses sparse set of "panoramic" depth maps or PDMs, each emulating an inward-viewing concentric mosaics. We then develop a learning-based technique to learn pixel-wise feature descriptors on PDMs. The results are fed into an autoencoder-based network for compression. Comprehensive experiments demonstrate our solution is robust and effective on both public and our newly captured datasets.