10 accepted papers
Multi-finger grasping relies on high quality training data, which is hard to obtain: human data is hard to transfer and synthetic data relies on simplifying assumptions that reduce grasp quality. By making grasp simulation differentiable, and contact dynamics amenable to gradient-based optimization,…
"The study of hand-object interaction requires generating viable grasp poses for high-dimensional multi-finger models, often relying on analytic grasp synthesis which tends to produce brittle and unnatural results. This paper presents Grasp’D, an approach to grasp synthesis by differentiable contact…
Differentiable rendering is an essential operation in modern vision, allowing inverse graphics approaches to 3D understanding to be utilized in modern machine learning frameworks. Yet, explicit shape representations (e.g., voxels, point clouds, meshes), while relatively easily rendered, often suffer…
Tool use requires reasoning about the fit between an object’s affordances and the demands of a task. Visual affordance learning can benefit from goal-directed interaction experience; but current techniques rely on human labels or expert demonstrations to generate this data. In this paper; we describ…
We combine ideas from shock graph theory with more recent appearance-based methods for medial axis extraction from complex natural scenes, improving upon the present best unsupervised method, in terms of efficiency and performance. We make the following specific contributions: i) we extend the shock…
Computing object skeletons in natural images is challenging, owing to large variations in object appearance and scale, and the complexity of handling background clutter. Many recent methods frame object skeleton detection as a binary pixel classification problem, which is similar in spirit to learni…
Representing 3D shapes is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics. One avenue that has recently begun to be explored is the use of latent representations of generative models. However, it remains an open problem to learn a…
The computer vision community has witnessed recent advances in scene categorization from images, with the state of the art systems now achieving impressive recognition rates on challenging benchmarks. Such systems have been trained on photographs which include color, texture and shading cues. The ge…
We introduce Appearance-MAT (AMAT), a generalization of the medial axis transform for natural images, that is framed as a weighted geometric set cover problem. We make the following contributions: i) we extend previous medial point detection methods for color images, by associating each medial point…
In recent years, region proposals have replaced sliding windows in support of object recognition, offering more discriminating shape and appearance information through improved localization. One powerful approach for generating region proposals is based on minimizing parametric energy functions wit…