5 research outputs found
Color-aware Deep Temporal Backdrop Duplex Matting System
Deep learning-based alpha matting showed tremendous improvements in recent years, yet, feature film production studios still rely on classical chroma keying including costly post-production steps. This perceived discrepancy can be explained by some missing links necessary for production which are currently not adequately addressed in the alpha matting community, in particular foreground color estimation or color spill compensation. We propose a neural network-based temporal multi-backdrop production system that combines beneficial features from chroma keying and alpha matting. Given two consecutive frames with different background colors, our one-encoder-dual-decoder network predicts foreground colors and alpha values using a patch-based overlap-blend approach. The system is able to handle imprecise backdrops, dynamic cameras, and dynamic foregrounds and has no restrictions on foreground colors. We compare our method to state-of-the-art algorithms using benchmark datasets and a video sequence captured by a demonstrator setup. We verify that a dual backdrop input is superior to the usually applied trimap-based approach. In addition, the proposed studio set is actor friendly, and produces high-quality, temporal consistent alpha and color estimations that include a superior color spill compensation
Unsupervised Multiple Person Tracking using AutoEncoder-Based Lifted Multicuts
Multiple Object Tracking (MOT) is a long-standing task in computer vision.
Current approaches based on the tracking by detection paradigm either require
some sort of domain knowledge or supervision to associate data correctly into
tracks. In this work, we present an unsupervised multiple object tracking
approach based on visual features and minimum cost lifted multicuts. Our method
is based on straight-forward spatio-temporal cues that can be extracted from
neighboring frames in an image sequences without superivison. Clustering based
on these cues enables us to learn the required appearance invariances for the
tracking task at hand and train an autoencoder to generate suitable latent
representation. Thus, the resulting latent representations can serve as robust
appearance cues for tracking even over large temporal distances where no
reliable spatio-temporal features could be extracted. We show that, despite
being trained without using the provided annotations, our model provides
competitive results on the challenging MOT Benchmark for pedestrian tracking
Learn to Model Blurry Motion via Directional Similarity and Filtering
It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects. In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. We first conduct a CNN architecture using a novel learnable directional filtering layer. Such layer encodes the angle and distance similarity matrix between blur and camera motion, which is able to enhance the blur features of the camera-shake footages. The proposed CNNs are then integrated into an iterative optical flow framework, which enable the capability of modeling and solving both the blind deconvolution and the optical flow estimation problems simultaneously. Our framework is trained end-to-end on a synthetic dataset and yields competitive precision and performance against the state-of-the-art approaches.</p