Capturing cloth motion with high fidelity is challenging due to fine-scale wrinkles, large deformation, and frequent self-occlusion. We present a 4D (spatio-temporal) cloth capture system that achieves 1 mm spatial resolution using only 16 RGB cameras. Our approach uses a two-level marker pattern: sparse, colored L-shaped markers provide robust detection and orientation, while dense noise patterns within each marker enable both marker identification and precise keypoint localization. By unwarping detected markers to a canonical frame, we factor out perspective distortion and most of cloth deformation, allowing the localizer to achieve sub-pixel accuracy. The localized keypoints are triangulated across views to form an incomplete point cloud. A physics-based optimization then deforms a template mesh to match the captured geometry while maintaining penetration-free constraints and physical plausibility for occluded regions. Our method produces temporally coherent sequences that faithfully capture fine wrinkles and folds even during complex motions with self-contact.

We present a novel contact model, termed Offset Geometric Contact (OGC), for guaranteed penetration-free simulation of codimensional objects with minimal computational overhead. Our method is based on constructing a volumetric shape by offsetting each face along its normal direction, ensuring orthogonal contact forces, thus allows large contact radius without artifacts. We compute vertex-specific displacement bounds to guarantee penetration-free simulation, which improves convergence and avoids the need for expensive continuous collision detection. Our method relies solely on massively parallel local operations, avoiding global synchronization and enabling efficient GPU implementation. Experiments demonstrate real-time, large-scale simulations with performance more than two orders of magnitude faster than prior methods while maintaining consistent computational budgets.

We introduce vertex block descent, a block coordinate descent solution for the variational form of implicit Euler through vertex-level Gauss-Seidel iterations. It operates with local vertex position updates that achieve reductions in global variational energy with maximized parallelism. This forms a physics solver that can achieve numerical convergence with unconditional stability and exceptional computation performance. It can also fit in a given computation budget by simply limiting the iteration count while maintaining its stability and superior convergence rate. We present and evaluate our method in the context of elastic body dynamics, providing details of all essential components and showing that it outperforms alternative techniques. In addition, we discuss and show examples of how our method can be used for other simulation systems, including particle-based simulations and rigid bodies.

We introduce a method for efficiently computing the exact shortest path to the boundary of a mesh from a given internal point in the presence of self-intersections. We provide a formal definition of shortest boundary paths for self-intersecting objects and present a robust algorithm for computing the actual shortest boundary path. The resulting method offers an effective solution for collision and self-collision handling while simulating deformable volumetric objects, using fast simulation techniques that provide no guarantees on collision resolution. Our evaluation includes complex self-collision scenarios with a large number of active contacts, showing that our method can successfully handle them by introducing a relatively minor computational overhead.