SIGGRAPH 2018

SIGGRAPH 2018 is just around the corner. Starting on Sunday August 12th in Vancouver, British Columbia and ending Thursday August 16th it’s looking like this is going to be a bumper year for presentations which cover applications of deep learning and demonstrate some truly stunning results. The full schedule is here, yet you can see…Read More

Texture Mixing by Interpolating Deep Statistics via Gaussian Models

Here’s an interesting technique which may provide new ways in which to generate and synthesize textures from examples via interpolation and style transfer. Recently, enthusiastic studies have devoted to texture synthesis using deep neural networks, because these networks excel at handling complex patterns in images. In these models, second-order statistics, such as Gram matrix, are…Read More

GRAINS: Generative Recursive Autoencoders for INdoor Scenes

Rule-based systems for procedural generation of complex environments such as interiors, cities, and landscapes have been around for a while. Here is a novel data-driven approach to not only learn how objects interact with each other in order to be able to produce new arrangements but also perform conversion of 2D floor plans to 3D…Read More

Production Deployment

You’ve carefully formulated and specified a target problem, collected and processed training data, picked a familiar framework (or learned a new one) and through a process of research, intuition, and luck chosen an initial network architecture to represent your problem. You’ve methodically run experiments and tuned hyperparameters, getting side-tracked along the way to write tools…Read More

Mesoscopic Facial Geometry Inference Using Deep Neural Networks

We present a learning-based approach for synthesizing facial geometry at medium and fine scales from diffusely-lit facial texture maps. When applied to an image sequence, the synthesized detail is temporally coherent. Unlike current state-of-the-art methods, which assume ”dark is deep”, our model is trained with measured facial detail collected using polarized gradient illumination in a…Read More

High-Fidelity Facial Reflectance and Geometry Inference From an Unconstrained Image

We present a deep learning-based technique to infer high-quality facial reflectance and geometry given a single unconstrained image of the sub- ject, which may contain partial occlusions and arbitrary illumination conditions. The reconstructed high-resolution textures, which are generated in only a few seconds, include high-resolution skin surface reflectance maps, representing both the diffuse and specular…Read More

Human Motion Modeling using DVGANs

We present a novel generative model for human motion modeling using Generative Adversarial Networks (GANs). We formulate the GAN discriminator using dense validation at each time-scale and perturb the discriminator input to make it translation invariant. Our model is capable of motion generation and completion. We show through our evaluations the resiliency to noise, generalization…Read More

Direction-aware Spatial Context Features for Shadow Detection and Removal

Shadow detection and shadow removal are fundamental and challenging tasks, requiring an understanding of the global image semantics. This paper presents a novel deep neural network design for shadow detection and removal by analyzing the image context in a direction-aware manner. To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent…Read More

PoseTrack

It’s always fun to see machine learning challenges posed as well as the competing solutions, but even more so when they’re relevant to VFX. PoseTrack is a new large-scale benchmark for human pose estimation and tracking in video. We provide a publicly available training and validation set as well as an evaluation server for benchmarking…Read More