Physics-based skin deformation methods can greatly improve the realism of character animation, but require non-trivial training, intensive manual intervention, and heavy numerical calculations. Due to these limitations, it is generally time-consuming to implement them, and difficult to achieve a high runtime efficiency. In order to tackle the above limitations caused by numerical calculations of physics-based skin deformation, we propose a simple and efficient analytical approach for physicsbased skin deformations. Specifically, we (1) employ Fourier series to convert 3D mesh models into continuous parametric representations through a conversion algorithm, which largely reduces data size and computing time but still keeps high realism, (2) introduce a partial differential equation (PDE)based skin deformation model and successfully obtain the first analytical solution to physics-based skin deformations which overcomes the limitations of numerical calculations. Our approach is easy to use, highly efficient, and capable to create physically realistic skin deformations.
Facial expression transfer is currently an active research field. However, 2D image wrapping based methods suffer from depth ambiguity and specific hardware is required for depth-based methods to work. We present a novel markerless, real time online facial transfer method that requires only a single video camera. Our method adapts a model to user specific facial data, computes expression variances in real time and rapidly transfers them to another target. Our method can be applied to videos without prior camera calibration and focal adjustment. It enables realistic online facial expression editing and performance transferring in many scenarios, such as: video conference; news broadcasting; lip-syncing for song performances; etc. With a low computational demand and hardware requirement, our method tracks a single user at an average of 38 fps. Our tracking method runs smoothly in web browsers despite their slower execution speed.
a b s t r a c tSketch-based human motion retrieval is a hot topic in computer animation in recent years. In this paper, we present a novel sketch-based human motion retrieval method via selected 2-dimensional (2D) Geometric Posture Descriptor (2GPD). Specially, we firstly propose a rich 2D pose feature call 2D Geometric Posture Descriptor (2GPD), which is effective in encoding the 2D posture similarity by exploiting the geometric relationships among different human body parts. Since the original 2GPD is of high dimension and redundant, a semi-supervised feature selection algorithm derived from Laplacian Score is then adopted to select the most discriminative feature component of 2GPD as feature representation, and we call it as selected 2GPD. Finally, a posture-by-posture motion retrieval algorithm is used to retrieve a motion sequence by sketching several key postures. Experimental results on CMU human motion database demonstrate the effectiveness of our proposed approach.
ProblemSketch as the most intuitive and powerful 2D design method has been used by artists for decades. However it is not fully integrated into current 3D animation pipeline as the difficulties of interpreting 2D line drawing into 3D. Posing 3D characters from 2D input is a complex and open problem. Related WorkGuay and colleagues use the line of action to pose the character with a single line to constrain the orientation of the skeleton structure. Our ApproachWe propose a new sketch based character posing system which is more flexible, efficient and it requires less input from the user. The character can be easily posed no matter the sketch represents a skeleton structure or shape contours. 3D Rigged Character Subset of vertices for matchingInput:-Character model V and its rigging p.-Sampled sketch:-Subset of vertices from the character mesh:including:* the points are around the outlines; * the points are lying close to the projection of the skeleton.To match the two point sets V and Y and meanwhile deform V to Y as closely as possible (finding p), we formulate it as solving the following optimization problem:such that:is the correspondance matrix, where the last row and column are introduced to handle outliers;-the second regularization term is used to add further constraints for searching candidate solutions in limited space;-the third term is used to prevent treating too many points as outliers.The objective function consists of:-a linear discrete assignment problem for correspondence;-a non-linear continuous problem for deformation.We can adopt an alternating strategy to solve the correspondence parameter ω and the rig parameter p:-By fixing p, we relax the binary ω to be a continuous valued matrix in [0, 1] and solve the relaxed subproblem by using Softassign and deterministic annealing.-By fixing ω, the enery function can be solved using a Newton-Raphson scheme. Results and Future workThe current system solves the posing parameters to match the input sketch and the rigged mesh. We intend to incorporate shape deformation into our energy function in the future, offering a more complete tool for character animation.
Motion capture is prevalent in the pipeline of realistic articulated character animation. To define accurate joint positions and joint orientations for the movement of a hierarchical human-like character without using a pre-defined skeleton remains a challenge for motion capture studios. In this paper, we present a method for automatically estimating and determining the topology of a hierarchical human skeleton from optical motion capture data based on the human biomechanical information. Through the use of a novel per-frame based recursive method with joint angle minimization, human skeleton mapping from optical markers and joint angle estimation are achieved in real time.
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