← ArchivePaper2019
Strand-Accurate Multi-View Hair Capture
First method to capture hair with sub-millimeter strand-level accuracy from a multi-view rig using slanted stereo correspondences and mean-shift strand growth.
Abstract
This paper presents a method to capture high-fidelity hair geometry with strand-level accuracy from multi-view images. In the first stage, a line-based PatchMatch multi-view stereo reformulates traditional MVS with a slanted strand-line assumption, using a cost function combining photo-consistency and a geometric term that reconstructs each hair pixel as a 3D line and merges the depth maps into a point cloud with per-point line directions. A mean-shift based strand reconstruction algorithm then converts the noisy point data into a set of strands, and a multi-view hair growing step elongates short strands and recovers missing ones. Evaluated on synthetic and real captured data, the method reconstructs hair strands with sub-millimeter accuracy and pixel-accurate projection to novel views.
How to read this
- Category
- Capture method: multi-view hair geometry reconstruction
- Contributions
- A line-based PatchMatch multi-view stereo that reconstructs each hair pixel as a 3D line under a slanted strand-line assumption
- A mean-shift strand reconstruction that converts noisy oriented point data into discrete strands
- A multi-view hair-growing step that elongates short strands and recovers missing ones, reported at sub-millimeter strand accuracy
- Context
- Builds on structure-aware hair capture (referenced Structure-Aware Hair Capture, Luo et al. 2013), reformulating traditional MVS with a strand-line model for finer geometry.Builds on: Structure-Aware Hair Capture
- Correctness
- Evaluated on synthetic and real captured data with sub-millimeter accuracy and pixel-accurate reprojection; relies on a multi-view rig and the slanted-line strand assumption, so results depend on capture setup and may degrade on very occluded or fine wispy regions not stressed in the stated evaluation.
- Clarity
- Clear staged pipeline; a first pass conveys the line-MVS plus growth idea, a second pass is needed for the cost function and mean-shift formulation.
- How to read it
- Read the three-stage pipeline structure first, then a second pass on the photo-consistency-plus-geometric cost and the strand-growing step if you work on capture; check the evaluation conditions before trusting the accuracy claim for your setup.
Builds on
Related work
- Robust Hair Capture Using Simulated Examples 2014 / SIGGRAPH
- Structure-Aware Hair Capture 2013 / SIGGRAPH
- Hair Modeling and Simulation by Style 2018 / CGF
- Fast Cloth Simulation on Moving Humanoids 2005 / Eurographics
Keywords
This page summarises the entry and links to its original source. The archive never hosts or redistributes the publication itself.Show it in the full archive list →