# Volumetric performance capture from minimal camera viewpoints

arXiv: https://arxiv.org/abs/1807.01950 · HTML: https://mlsanity.com/paper/1807.01950/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2018.

## Built on by (most cited first, 17 of 17)

- [ARCH++: Animation-Ready Clothed Human Reconstruction Revisited](https://mlsanity.com/paper/2108.07845/) (2021)
- [Structured Local Radiance Fields for Human Avatar Modeling](https://mlsanity.com/paper/2203.14478/) (2022)
- [DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras](https://mlsanity.com/paper/2105.00261/) (2021)
- [DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras](https://mlsanity.com/paper/2207.08000/) (2022)
- [StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision](https://mlsanity.com/paper/2104.05289/) (2021)
- [M3D-VTON: A Monocular-to-3D Virtual Try-On Network](https://mlsanity.com/paper/2108.05126/) (2021)
- [JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction](https://mlsanity.com/paper/2204.10549/) (2022)
- [GaussianVTON: 3D Human Virtual Try-ON via Multi-Stage Gaussian Splatting Editing with Image Prompting](https://mlsanity.com/paper/2405.07472/) (2024)
- [SeSDF: Self-evolved Signed Distance Field for Implicit 3D Clothed Human Reconstruction](https://mlsanity.com/paper/2304.00359/) (2023)
- [Guide3D: Create 3D Avatars from Text and Image Guidance](https://mlsanity.com/paper/2308.09705/) (2023)
- [LightSAL: Lightweight Sign Agnostic Learning for Implicit Surface Representation](https://mlsanity.com/paper/2103.14273/) (2021)
- [Learning Implicit Body Representations from Double Diffusion Based Neural Radiance Fields](https://mlsanity.com/paper/2112.12390/) (2021)
- [High-Resolution Volumetric Reconstruction for Clothed Humans](https://mlsanity.com/paper/2307.13282/) (2023)
- [Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded Images](https://mlsanity.com/paper/2409.18364/) (2024)
- [Temporal Consistency Loss for High Resolution Textured and Clothed 3DHuman Reconstruction from Monocular Video](https://mlsanity.com/paper/2104.09259/) (2021)
- [MVP-Human Dataset for 3D Human Avatar Reconstruction from Unconstrained Frames](https://mlsanity.com/paper/2204.11184/) (2022)
- [FAMOUS: High-Fidelity Monocular 3D Human Digitization Using View Synthesis](https://mlsanity.com/paper/2410.09690/) (2024)

## Abstract (arXiv)

We present a convolutional autoencoder that enables high fidelity volumetric reconstructions of human performance to be captured from multi-view video comprising only a small set of camera views. Our method yields similar end-to-end reconstruction error to that of a probabilistic visual hull computed using significantly more (double or more) viewpoints. We use a deep prior implicitly learned by the autoencoder trained over a dataset of view-ablated multi-view video footage of a wide range of subjects and actions. This opens up the possibility of high-end volumetric performance capture in on-set and prosumer scenarios where time or cost prohibit a high witness camera count.