Anthropometric clothing measurements from 3D body scans

We propose a full processing pipeline to acquire anthropometric measurements from 3D measurements. The first stage of our pipeline is a commercial point cloud scanner. In the second stage, a pre-defined body model is fitted to the captured point cloud. We have generated one male and one female model from the SMPL library. The fitting process is based on non-rigid Iterative Closest Point (ICP) algorithm that minimizes overall energy of point distance and local stiffness energy terms. In the third stage, we measure multiple circumference paths on the fitted model surface and use a non-linear regressor to provide the final estimates of anthropometric measurements. We scanned 194 male and 181 female subjects and the proposed pipeline provides mean absolute errors from 2.5 mm to 16.0 mm depending on the anthropometric measurement

Fast Fourier Intrinsic Network

We address the problem of decomposing an image into albedo and shading. We propose the Fast Fourier Intrinsic Network, FFI-Net in short, that operates in the spectral domain, splitting the input into several spectral bands. Weights in FFI-Net are optimized in the spectral domain, allowing faster convergence to a lower error. FFI-Net is lightweight and does not need auxiliary networks for training. The network is trained end-to-end with a novel spectral loss which measures the global distance between the network prediction and corresponding ground truth. FFI-Net achieves state-of-the-art performance on MPI-Sintel, MIT Intrinsic, and IIW datasets.Comment: WACV 2021 - camera read

Single Pixel Spectral Color Constancy

Color constancy is still one of the biggest challenges in camera color processing. Convolutional neural networks have been able to improve the situation but there are still problems in many conditions, especially in scenes where a single color is dominating. In this work, we approach the problem from a slightly different setting. What if we could have some other information than the raw RGB image data. What kind of information would help to bring significant improvements while still be feasible in a mobile device. These questions sparked an idea for a novel approach for computational color constancy. Instead of raw RGB images used by the existing algorithms to estimate the scene white points, our approach is based on the scene’s average color spectra-single pixel spectral measurement. We show that as few as 10–14 spectral channels are sufficient. Notably, the sensor output has five orders of magnitude less data than in raw RGB images of a 10MPix camera. The spectral sensor captures the “spectral fingerprints” of different light sources and the illuminant white point can be accurately estimated by a standard regressor. The regressor can be trained with generated measurements using the existing RGB color constancy datasets. For this purpose, we propose a spectral data generation pipeline that can be used if the dataset camera model is known and thus its spectral characterization can be obtained. To verify the results with real data, we collected a real spectral dataset with a commercial spectrometer. On all datasets the proposed Single Pixel Spectral Color Constancy obtains the highest accuracy in the both single and cross-dataset experiments. The method is particularly effective for the difficult scenes for which the average improvements are 40–70% compared to state-of-the-arts. The approach can be extended to multi-illuminant case for which the experimental results also provide promising results.Peer reviewe

Silhouette Body Measurement Benchmarks

Anthropometric body measurements are importantfor industrial design, garment fitting, medical diagnosis andergonomics. A number of methods have been proposed toestimate the body measurements from images, but progress hasbeen slow due to the lack of realistic and publicly availabledatasets. The existing works train and test on silhouettes of3D body meshes obtained by fitting a human body model tothe commercial CAESAR scans. In this work, we introduce theBODY-fit dataset that contains fitted meshes of 2,675 female and1,474 male 3D body scans. We unify evaluation on the CAESAR-fit and BODY-fit datasets by computing body measurements fromgeodesic surface paths as the ground truth and by generating two-view silhouette images. We also introduce BODY-rgb - a realisticdataset of 86 male and 108 female subjects captured with an RGBcamera and manually tape measured ground truth. We propose asimple yet effective deep CNN architecture as a baseline methodwhich obtains competitive accuracy on the three datasets.acceptedVersionPeer reviewe

Active Short-Long Exposure Deblurring

Mobile phones can capture image bursts to produce high quality still photographs. The simplest form of a burst is two frame short-long (S-L) exposure. S-L exposure is particularly suitable in low light conditions where short exposure frames are sharp but noisy and dark, and long exposure frames are affected by motion blur but have better scene chromaticity and luminance. In this work, we take a step further and define active short-long exposure deblurring where the viewfinder frames before the burst are used to optimize the S-L exposure parameters. We introduce deep architectures and data generation for active S-L exposure deblurring. The approach is experimentally validated with realistic data and it shows clear improvements. For the most difficult scenes (worst 5%) the PSNR is improved by +1.39dB.acceptedVersionPeer reviewe