Bernard Tiddeman

Hancheng Zuo, Bernard Tiddeman

This study introduces a novel method for inpainting normal maps using a generative adversarial network (GAN). Normal maps, often derived from a lightstage, are crucial in performance capture but can have obscured areas due to movement (e.g., by arms, hair, or props). Inpainting fills these missing areas with plausible data. Our approach extends previous general image inpainting techniques, employing a bow tie-like generator network and a discriminator network, with alternating training phases. The generator aims to synthesize images aligning with the ground truth and deceive the discriminator, which differentiates between real and processed images. Periodically, the discriminator undergoes retraining to enhance its ability to identify processed images. Importantly, our method adapts to the unique characteristics of normal map data, necessitating modifications to the loss function. We utilize a cosine loss instead of mean squared error loss for generator training. Limited training data availability, even with synthetic datasets, demands significant augmentation, considering the specific nature of the input data. This includes appropriate image flipping and in-plane rotations to accurately alter normal vectors. Throughout training, we monitored key metrics such as average loss, Structural Similarity Index Measure (SSIM), and Peak Signal-to-Noise Ratio (PSNR) for the generator, along with average loss and accuracy for the discriminator. Our findings suggest that the proposed model effectively generates high-quality, realistic inpainted normal maps, suitable for performance capture applications. These results establish a foundation for future research, potentially involving more advanced networks and comparisons with inpainting of source images used to create the normal maps.

William A. P. Smith, Alassane Seck, Hannah Dee et al.

In this paper, we bring together two divergent strands of research: photometric face capture and statistical 3D face appearance modelling. We propose a novel lightstage capture and processing pipeline for acquiring ear-to-ear, truly intrinsic diffuse and specular albedo maps that fully factor out the effects of illumination, camera and geometry. Using this pipeline, we capture a dataset of 50 scans and combine them with the only existing publicly available albedo dataset (3DRFE) of 23 scans. This allows us to build the first morphable face albedo model. We believe this is the first statistical analysis of the variability of facial specular albedo maps. This model can be used as a plug in replacement for the texture model of the Basel Face Model (BFM) or FLAME and we make the model publicly available. We ensure careful spectral calibration such that our model is built in a linear sRGB space, suitable for inverse rendering of images taken by typical cameras. We demonstrate our model in a state of the art analysis-by-synthesis 3DMM fitting pipeline, are the first to integrate specular map estimation and outperform the BFM in albedo reconstruction.

Alassane Seck, William A. P. Smith, Arnaud Dessein et al.

We present a practical approach to capturing ear-to-ear face models comprising both 3D meshes and intrinsic textures (i.e. diffuse and specular albedo). Our approach is a hybrid of geometric and photometric methods and requires no geometric calibration. Photometric measurements made in a lightstage are used to estimate view dependent high resolution normal maps. We overcome the problem of having a single photometric viewpoint by capturing in multiple poses. We use uncalibrated multiview stereo to estimate a coarse base mesh to which the photometric views are registered. We propose a novel approach to robustly stitching surface normal and intrinsic texture data into a seamless, complete and highly detailed face model. The resulting relightable models provide photorealistic renderings in any view.