Manifold clustering is an important problem in motion and video segmentation, natural image clustering, and other applications where high-dimensional data lie on multiple, low-dimensional, nonlinear manifolds.

Overall, our method can generate high-fidelity, diverse, and multi-view consistent meshes from single-view wild images within 30 seconds, as shown in Figure 1. We conduct extensive experiments on various wild 2D images with different styles.

For iterative algorithms, implicit differentiation alleviates this issue but requires custom implementation of Jacobian evaluation. In this paper, we study one-step differentiation, also known as Jacobian-free backpropagation, a method as easy as automatic differentiation and as efficient as implicit differentiation for fast algorithms (e.g., superlinear

These models excel in zero-shot and few-shot learning, enabling them to learn new tasks without parameter updates.

To tackle this issue with a common benchmark, we introduce the Drunkard's