This paper systematically describes an interactive dissection approach for hybrid soft tissue models governed by extended position-based dynamics. Our framework makes use of a hybrid geometric model comprising both surface and volumetric meshes. The fine surface triangular mesh with high-precision geometric structure and texture at the detailed level is employed to represent the exterior structure of soft tissue models. Meanwhile, the interior structure of soft tissues is constructed by coarser tetrahedral mesh, which is also employed as physical model participating in dynamic simulation. The less details of interior structure can effectively reduce the computational cost during simulation. For physical deformation, we design and implement an extended position-based dynamics approach that supports topology modification and material heterogeneities of soft tissue. Besides stretching and volume conservation constraints, it enforces the energy preserving constraints, which take the different spring stiffness of material into account and improve the visual performance of soft tissue deformation. Furthermore, we develop mechanical modeling of dissection behavior and analyze the system stability. The experimental results have shown that our approach affords real-time and robust cutting without sacrificing realistic visual performance. Our novel dissection technique has already been integrated into a virtual reality-based laparoscopic surgery simulator.
Existing keyframe-based motion synthesis mainly focuses on the generation of cyclic actions or short-term motion, such as walking, running, and transitions between close postures. However, these methods will significantly degrade the naturalness and diversity of the synthesized motion when dealing with complex and impromptu movements, e.g., dance performance and martial arts. In addition, current research lacks fine-grained control over the generated motion, which is essential for intelligent human-computer interaction and animation creation. In this paper, we propose a novel keyframe-based motion generation network based on multiple constraints, which can achieve diverse dance synthesis via learned knowledge. Specifically, the algorithm is mainly formulated based on the recurrent neural network (RNN) and the Transformer architecture. The backbone of our network is a hierarchical RNN module composed of two long short-term memory (LSTM) units, in which the first LSTM is utilized to embed the posture information of the historical frames into a latent space, and the second one is employed to predict the human posture for the next frame. Moreover, our framework contains two Transformer-based controllers, which are used to model the constraints of the root trajectory and the velocity factor respectively, so as to better utilize the temporal context of the frames and achieve fine-grained motion control. We verify the proposed approach on a dance dataset containing a wide range of contemporary dance. The results of three quantitative analyses validate the superiority of our algorithm. The video and qualitative experimental results demonstrate that the complex motion sequences generated by our algorithm can achieve diverse and smooth motion transitions between keyframes, even for long-term synthesis.
Laparoscopic surgery is a complex minimum invasive operation which requires long learning curve for the new trainees to get adequate experience to become a qualified surgeon. With the development of virtual reality technology, VR based surgery simulation is playing increasingly important role in the surgery training. The simulation of laparoscopic surgery is challenging because it involves large nonlinear soft * e-mail:pan junjun@hotmail.com 1 tissue deformation, frequent surgical tool interaction and complex anatomical environment. Current researches mostly focus on very specific topics (such as deformation, collision detection etc.) rather than a consistent and efficient framework. The direct use of the existing methods cannot achieve high visual/haptic quality and a satisfactory refreshing rate at the same time, especially for complex surgery simulation. In this paper, we proposed a set of tailored key technologies for laparoscopic surgery simulation, ranging from the simulation of soft tissues with different properties, the interactions between surgical tools and soft tissues and the rendering of complex anatomical environment. Compared to the current methods, our tailored algorithms aimed at improving the performance from accuracy, stability and efficiency perspectives. We also abstract and design a set of intuitive parameters which can provide developers with high flexibility to develop their own simulators.
The rapid creation of 3D character animation by commodity devices plays an important role in enriching visual content in virtual reality. This paper concentrates on addressing the challenges of current motion imitation for human body. We develop an interactive framework for stable motion capturing and animation generation based on single Kinect device. In particular, we focus our research efforts on two cases: (1) The participant is facing the camera; or (2) the participant is turning around or is side facing the camera. Using existing methods, camera could obtain a profile view of the body, but it frequently leads to less satisfactory result or even failure due to occlusion. In order to reduce certain artifacts appeared at the side view, we design a mechanism to refine the movement of the human body by integrating an adaptive filter. After specifying the corresponding joints between the participant and the virtual character, the captured motion could be retargeted in a quaternion-based manner. To further improve the animation quality, inverse kinematics are brought into our framework to constrain the target's positions. A large variety of motions and characters have been tested to validate the performance of our framework. Through experiments, it shows that our method could be applied to real-time applications, such as physical therapy and fitness training.
This paper describes an interactive dissection approach for hybrid soft tissue models governed by position-based dynamics. Our framework makes use of a hybrid geometric model comprising both surface and volumetric meshes. The fine surface triangular mesh is used to represent the exterior structure of soft tissue models. Meanwhile, the interior structure of soft tissues is constructed by coarser tetrahedral meshes, which are also employed as physical models participating in dynamic simulation. The less details of interior structure can effectively reduce the computational cost of deformation and geometric subdivision during dissection. For physical deformation, we design and implement a position-based dynamics approach that supports topology modification and enforces the volume-preserving constraint. Experimental results have shown that, this hybrid dissection method affords real-time and robust cutting simulation without sacrificing realistic visual performance.
Similar to language and music, dance performances provide an effective way to express human emotions. With the abundance of the motion capture data, content‐based motion retrieval and classification have been fiercely investigated. Although researchers attempt to interpret body language in terms of human emotions, the progress is limited by the scarce 3D motion database annotated with emotion labels. This article proposes a hybrid feature for emotional classification in dance performances. The hybrid feature is composed of an explicit feature and a deep feature. The explicit feature is calculated based on the Laban movement analysis, which considers the body, effort, shape, and space properties. The deep feature is obtained from latent representation through a 1D convolutional autoencoder. Eventually, we present an elaborate feature fusion network to attain the hybrid feature that is almost linearly separable. The abundant experiments demonstrate that our hybrid feature is superior to the separate features for the emotional classification in dance performances.
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