Jian Ma

Yi Wang, Jian Ma, Ruizhi Shao et al.

The generation of 3D clothed humans has attracted increasing attention in recent years. However, existing work cannot generate layered high-quality 3D humans with consistent body structures. As a result, these methods are unable to arbitrarily and separately change and edit the body and clothing of the human. In this paper, we propose a text-driven layered 3D human generation framework based on a novel physically-decoupled semantic-aware diffusion model. To keep the generated clothing consistent with the target text, we propose a semantic-confidence strategy for clothing that can eliminate the non-clothing content generated by the model. To match the clothing with different body shapes, we propose a SMPL-driven implicit field deformation network that enables the free transfer and reuse of clothing. Besides, we introduce uniform shape priors based on the SMPL model for body and clothing, respectively, which generates more diverse 3D content without being constrained by specific templates. The experimental results demonstrate that the proposed method not only generates 3D humans with consistent body structures but also allows free editing in a layered manner. The source code will be made public.

Shashank Singh, Barnabás Póczos, Jian Ma

Sparse dictionary learning (SDL) has become a popular method for adaptively identifying parsimonious representations of a dataset, a fundamental problem in machine learning and signal processing. While most work on SDL assumes a training dataset of independent and identically distributed samples, a variant known as convolutional sparse dictionary learning (CSDL) relaxes this assumption, allowing more general sequential data sources, such as time series or other dependent data. Although recent work has explored the statistical properties of classical SDL, the statistical properties of CSDL remain unstudied. This paper begins to study this by identifying the minimax convergence rate of CSDL in terms of reconstruction risk, by both upper bounding the risk of an established CSDL estimator and proving a matching information-theoretic lower bound. Our results indicate that consistency in reconstruction risk is possible precisely in the `ultra-sparse' setting, in which the sparsity (i.e., the number of feature occurrences) is in $o(N)$ in terms of the length N of the training sequence. Notably, our results make very weak assumptions, allowing arbitrary dictionaries and dependent measurement noise. Finally, we verify our theoretical results with numerical experiments on synthetic data.