DSpace Collection:
http://hdl.handle.net/10722/57787
2024-03-28T08:20:27ZA Deep Learning Framework for Character Motion Synthesis and Editing
http://hdl.handle.net/10722/340482
Title: A Deep Learning Framework for Character Motion Synthesis and Editing
Authors: Holden, Daniel; Saito, Jun; Komura, Taku
Abstract: <p> We present a framework to synthesize character movements based on high level parameters, such that the produced movements respect the manifold of human motion, trained on a large motion capture dataset. The learned motion manifold, which is represented by the hidden units of a convolutional autoencoder, represents motion data in sparse components which can be combined to produce a wide range of complex movements. To map from high level parameters to the motion manifold, we stack a deep feedforward neural network on top of the trained autoencoder. This network is trained to produce realistic motion sequences from parameters such as a curve over the terrain that the character should follow, or a target location for punching and kicking. The feedforward control network and the motion manifold are trained independently, allowing the user to easily switch between feedforward networks according to the desired interface, without re-training the motion manifold. Once motion is generated it can be edited by performing optimization in the space of the motion manifold. This allows for imposing kinematic constraints, or transforming the style of the motion, while ensuring the edited motion remains natural. As a result, the system can produce smooth, high quality motion sequences without any manual pre-processing of the training data. <br></p>2023-08-02T00:00:00ZGen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images
http://hdl.handle.net/10722/340432
Title: Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images
Authors: Liu, Yuan; Wen, Yilin; Peng, Sida; Lin, Cheng; Long, Xiaoxiao; Komura, Taku; Wang, Wenping
Abstract: <p>In this paper, we present a generalizable model-free 6-DoF object pose estimator called Gen6D. Existing generalizable pose estimators either need the high-quality object models or require additional depth maps or object masks in test time, which significantly limits their application scope. In contrast, our pose estimator only requires some posed images of the unseen object and is able to accurately predict poses of the object in arbitrary environments. Gen6D consists of an object detector, a viewpoint selector and a pose refiner, all of which do not require the 3D object model and can generalize to unseen objects. Experiments show that Gen6D achieves state-of-the-art results on two model-free datasets: the MOPED dataset and a new GenMOP dataset. In addition, on the LINEMOD dataset, Gen6D achieves competitive results compared with instance-specific pose estimators. Project page: <a href="https://liuyuan-pal.github.io/Gen6D/">https://liuyuan-pal.github.io/Gen6D/.</a></p>2022-11-11T00:00:00ZOptimization-Based Online Flow Fields Estimation for AUVs Navigation
http://hdl.handle.net/10722/340429
Title: Optimization-Based Online Flow Fields Estimation for AUVs Navigation
Authors: Xu, Hao; Lu, Yupu; Pan, Jia
Abstract: <p>The motion of an autonomous underwater vehicle (AUV) is affected by its surrounding water flows, so an accurate estimation of the flow field could be used to assist the vehicle’s navigation. We propose an optimization-based approach to the problem of online flow field learning with limited amounts of data. To compensate for the shortage of online measurements, we identify two types of physically meaningful constraints from eddy geometry of the flow field and the property of fluid incompressibility respectively. By parameterizing the flow field as a polynomial vector field, the optimization problem could be solved efficiently via semi-definite programming (SDP). The effectiveness of the proposed algorithm in terms of flow field estimation is experimentally validated on real-world ocean data by providing performance comparisons with a related method. Further, the proposed estimation algorithm is proved to be able to be combined with a motion planning method to allow an AUV to navigate efficiently in an underwater environment where the flow field is unknown beforehand.</p>2023-03-08T00:00:00ZApplications of Online Matching
http://hdl.handle.net/10722/339121
Title: Applications of Online Matching
Authors: Huang, Zhiyi; Trobst, Thorben2023-05-13T00:00:00Z