Course about deep learning for computer vision and graphics co-developed by YSDA and Skoltech.

Deep Learning Computer Vision models depend on massive datasets for their advance computations. There are multiple human motion capture datasets available which serves the purpose of doing the base computations but lack to be the ideal dataset in two major ways. Either, the datasets are very small and are constrained to only particular motion or the output generated by these datasets is not natural, that is, it’s not true to our humane movements. These problems are solved by AMASS (Archive of Motion Capture as Surface Shapes) which generates highly versatile SMPL (Multi Person Linear Model) model outputs which includes standard skeletal representation and a full surface body mesh. The MoSh algorithm which is used to generate the output is modified to incorporate the soft tissue dynamics and renamed to MoSh++. The dataset is then made richer by adding data from datasets like DMPL (Dynamic SMPL) and MANO (Hand Model with Articulated and Non-rigid Deformations). AMASS is able to generate output which is compatible with recent graphics engines, game engines and animations requirements.

Simulation of Predator Prey Dynamics using Deep Reinforcement Learning (CS 275: Artificial Life for Computer Graphics and Vision - Course Project)

In the late 1960s, computer vision began at universities which were pioneering artificial intelligence. It was meant to mimic the human visual system, as a stepping stone to endowing robots with intelligent behavior.[11] In 1966, it was believed that this could be achieved through a summer project, by attaching a camera to a computer and having it "describe what it saw".[12][13] What distinguished computer vision from the prevalent field of digital image processing at that time was a desire to extract three-dimensional structure from images with the goal of achieving full scene understanding. Studies in the 1970s formed the early foundations for many of the computer vision algorithms that exist today, including extraction of edges from images, labeling of lines, non-polyhedral and polyhedral modeling, representation of objects as interconnections of smaller structures, optical flow, and motion estimation.[11] The next decade saw studies based on more rigorous mathematical analysis and quantitative aspects of computer vision. These include the concept of scale-space, the inference of shape from various cues such as shading, texture and focus, and contour models known as snakes. Researchers also realized that many of these mathematical concepts could be treated within the same optimization framework as regularization and Markov random fields.[14] By the 1990s, some of the previous research topics became more active than the others. Research in projective 3-D reconstructions led to better understanding of camera calibration. With the advent of optimization methods for camera calibration, it was realized that a lot of the ideas were already explored in bundle adjustment theory from the field of photogrammetry. This led to methods for sparse 3-D reconstructions of scenes from multiple images. Progress was made on the dense stereo correspondence problem and further multi-view stereo techniques. At the same time, variations of graph cut were used to solve image segmentation. This decade also marked the first time statistical learning techniques were used in practice to recognize faces in images (see Eigenface). Toward the end of the 1990s, a significant change came about with the increased interaction between the fields of computer graphics and computer vision. This included image-based rendering, image morphing, view interpolation, panoramic image stitching and early light-field rendering.[11] Recent work has seen the resurgence of feature-based methods, used in conjunction with machine learning techniques and complex optimization frameworks.[15][16] The advancement of Deep Learning techniques has brought further life to the field of computer vision. The accuracy of deep learning algorithms on several benchmark computer vision data sets for tasks ranging from classification, segmentation and optical flow has surpassed prior methods.[citation needed]

Job info platform in industry and academia covered Computer Vision, Deep Learning, Robotics, NLP, Graphics.

course at YSDA

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Assignment №5 from course "Deep Vision and Graphics" taught at YSDA (https://github.com/yandexdataschool/deep_vision_and_graphics)

Deep Learning for Computer Vision and Graphics

We are working on Deep Learning, Machine Learning, Computer Vision and Computer Graphics.

Contains projects from computer graphics and vision, covering topics such as robust model fitting, image deformation and compression, interactive segmentation, image denoising, and deep learning for super-resolution.

3D Reconstructor is a computer vision project that reconstructs 3D models from 2D images using deep learning and multi-view geometry techniques. This project explores the intersection of image processing, neural networks, and 3D graphics to generate detailed and realistic 3D representations from flat input images.