The solution of large sparse systems of linear constraints is at the base of most interactive solvers for physically-based animation of soft body dynamics. We focus on applications with hard and tight per-frame resource budgets, such as video games, where the solution of soft body dynamics needs to be computed in a few milliseconds. Linear iterative methods are preferred in these cases since they provide approximate solutions within a given error tolerance and in a short amount of time. We present a parallel randomized Gauss-Seidel method which can be effectively employed to enable the animation of 3D soft objects discretized as large and irregular triangular or tetrahedral meshes. At the beginning of each frame, we partition the set of equations governing the system using a randomized graph coloring algorithm. The unknowns in the equations belonging to the same partition are independent of each other. Then, all the equations belonging to the same partition are solved at the same time in parallel. Our algorithm runs completely on the GPU and can support changes in the constraints topology. We tested our method as a solver for soft body dynamics within the Projective Dynamics and Position Based Dynamics frameworks. We show how the algorithmic simplicity of this iterative strategy enables great numerical stability and fast convergence speed, which are essential features for physically based animations with fixed and small hard time budgets. Compared to the state of the art, we found our method to be faster and scale better while providing stabler solutions for very small time budgets.

Mesh editing software is improving, allowing skilled artists to create detailed meshes efficiently. For a variety of reasons, artists are interested in sharing not just their final mesh but also their whole workflow, though the common media for sharing has limitations. In this paper, we present 3DFlow , an algorithm that computes continuous summarizations of mesh editing workflows. 3DFlow takes as input a sequence of meshes and outputs a visualization of the workflow summarized at any level of detail. The output is enhanced by highlighting edited regions and, if provided, overlaying visual annotations to indicated the artist's work, e.g. summarizing brush strokes in sculpting. We tested 3DFlow with a large set of inputs using a variety of mesh editing techniques, from digital sculpting to low-poly modeling, and found 3DFlow performed well for all. Furthermore, 3DFlow is independent of the modeling software used because it requires only mesh snapshots, and uses the additional information only for optional overlays. We release 3DFlow as open source for artists to showcase their work and release all our datasets so other researchers can improve upon our work.

Current modeling packages have polished interfaces for editing polygonal meshes, where artists work individually on each mesh. A variety of recent cloud-based services have shown the benefits of editing documents in real-time collaboration with others. In this paper, we present a system for collaborative editing of low-polygonal and subdivision mesh models. We cast collaborative editing as a special instance of distributed version control. We support concurrent editing by robustly sharing and merging mesh version histories in real-time. We store and transmit mesh differences efficiently by encoding them as sequences of primitive editing operations. We enable collaboration by merging and detecting conflicts. We extend this model letting artists adapt others' editing histories by retargeting sequences of editing operations to new parts of the mesh with potentially different topology. We tested our algorithms by editing meshes with up to thousand edits, in collaborative editing sessions lasting a few hours, and by retargeting sequences of several hundred edits. We found the proposed system to be reliable, fast and scaling well with mesh complexity. We demonstrate that our merge algorithm is more robust than prior work. We further validated the proposed collaborative workflow with a user study where MeshHisto was consistently preferred over other alternatives for collaborative workflows.