For the motion control module in ARTEMIS, we combine state-of-the-art animal motion capture approach with neural character control scheme. Finally, we propose a novel shading network to generate high-fidelity details of appearance and opacity under novel poses. We further use a fast octree indexing, an efficient volumetric rendering scheme to generate appearance and density features maps. The animation then becomes equivalent to voxel level skeleton based deformation. The core of ARTEMIS is a neural-generated (NGI) animal engine, which adopts an efficient octree based representation for animal animation and fur rendering. Our ARTEMIS enables interactive motion control, real-time animation and photo-realistic rendering of furry animals.
In this paper, we present ARTEMIS, a novel neural modeling and rendering pipeline for generating ARTiculated neural pets with appEarance and Motion synthesIS. Yet, computer-generated (CGI) furry animals is limited by tedious off-line rendering, let alone interactive motion control. We human are entering into a virtual era, and surely want to bring animals to virtual world as well for companion. Overall, these results demonstrate that open-source, markerless methods are a promising new tool for analyzing human motion. However, excellent agreement was found between the segment calculation methods, with mean differences ≤1° and intraclass correlation coefficients ≥.90.
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Compared to manual digitization, the markerless method was found to systematically overestimate foot angles and underestimate tibial angles ( P <. The train/test errors for the trained network were 2.87/7.79 pixels, respectively (0.5/1.2 cm). Bland–Altman plots and paired t tests were used to assess systematic bias. Agreement was assessed with mean absolute differences and intraclass correlation coefficients. Foot and tibia angles were calculated for 7 strides using manual digitization and markerless methods.
Overall network accuracy was assessed using the train/test errors. The trained model was used to process novel videos from 34 participants for continuous 2D coordinate data. Data from 50 participants were used to train a deep neural network for 2D pose estimation of the foot and tibia segments. Eighty-four runners who had sagittal plane videos recorded of their left lower leg were included in the study.
We sought to establish the performance of one of these platforms, DeepLabCut. Several open-source platforms for markerless motion capture offer the ability to track 2-dimensional (2D) kinematics using simple digital video cameras.
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