As the quantity of 3D assets created by studios steadily grows there will surely be increased desire to maximize reuse of models from previous productions for set dressing, and so on. Could this efficiency push lead to entirely new ways of interacting with asset management systems? We present 3D Pick & Mix, a new 3D…Read More
Tag: 3d
Peeking Behind Objects: Layered Depth Prediction from a Single Image
Here’s a nice approach to depth estimation which combines in-painting to allow simulation of slight camera moves from just a single image. While conventional depth estimation can infer the geometry of a scene from a single RGB image, it fails to estimate scene regions that are occluded by foreground objects. This limits the use of…Read More
Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling
We present a method for simultaneously estimating 3D human pose and body shape from a sparse set of wide-baseline camera views. We train a symmetric convolutional autoencoder with a dual loss that enforces learning of a latent representation that encodes skeletal joint positions, and at the same time learns a deep representation of volumetric body…Read More
Deep Generative Modeling for Scene Synthesis via Hybrid Representations
A recent post demonstrated a data-driven system for indoor scene synthesis. This work is motivated by similar but is noteworthy not only for its results – which allows for repeated inclusion of a given type of object and interpolation of entire scenes – but also the rigorous analysis of their approach which is bound to…Read More
Deep Learning Advances on Different 3D Data Representations: A Survey
Much of the attention surrounding deep learning and its impact on visual effects has focused on image-based techniques, in particular the compelling examples produced by denoising, segmentation, and generative adversarial models. Research continues, however, into application of machine learning to 3D data – meshes, point clouds, voxel grids, and so on – and how to…Read More
Joint Learning of Intrinsic Images and Semantic Segmentation
We’ve previously covered both semantic segmentation and intrinsic image decomposition. Here we see a novel proposal to combine the two tasks under the premise that knowledge of one can assist the other. Models and datasets are coming soon. Semantic segmentation of outdoor scenes is problematic when there are variations in imaging conditions. It is known…Read More
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
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