Yuezhi Yang

Vector displacement map is a common tool for artists to add parts or surface details to 3D shapes. However, creating VDM is tedious as it requires artists enormous effort to model 3D shapes, Despite the growing popularity of text/image to 3D object model, most of them has fail to generate details comparable to VDM. In this project, we present a system to automatically generate a VDM conditioned on text prompt or an image. To tackle the challenge of VDM data scarcity, we propose a multi-view normal image generation model that fineturn stable diffusion model on part-level objaverse data. We then build a reconstruction model to directly reconstruct VDM from multi-view data. Preliminary result shows that our model outperforms text/image to 3D models in detail generation.

We introduce GenAnalysis, an implicit shape generation framework that allows joint analysis of a collection of man-made shapes. GenAnalysis innovates in learning an implicit shape generator to reconstruct a continuous shape space from the input shape collection. It offers interpolations between pairs of input shapes for correspondence computation. It also allows us to understand the shape variations of each shape in the context of neighboring shapes. Such variations provide segmentation cues. A key idea of GenAnalysis is to enforce an as-affine-as-possible (AAAP) deformation regularization loss among adjacent synthetic shapes of the generator. This loss forces the generator to learn the underlying piece-wise affine part structures. We show how to extract data-driven segmentation cues by recovering piece-wise affine vector fields in the tangent space of each shape and how to use this generator to compute consistent inter-shape correspondences. These correspondences are then used to aggregate single-shape segmentation cues into consistent segmentations. Experimental results on benchmark datasets show that GenAnalysis achieves state-of-the-art results on shape segmentation and shape matching.

Sketch design concepts are recurring patterns found in parametric CAD sketches.Though rarely explicitly formalized by the CAD designers, these concepts are implicitly used in design for modularity and regularity. In this paper, we propose a learning based approach that discovers the modular concepts by induction over raw sketches. We propose the dual implicit-explicit representation of concept structures that allows implicit detection and explicit generation, and the separation of structure generation and parameter instantiation for parameterized concept generation, to learn modular concepts by end-to-end training. We demonstrate the design concept learning on a large scale CAD sketch dataset and show its applications for design intent interpretation and auto-completion.

In orthodontic treatment, a full tooth model consisting of both the crown and root is indispensable in making the treatment plan. However, acquiring tooth root information to obtain the full tooth model from CBCT images is sometimes restricted due to the massive radiation of CBCT scanning. Thus, reconstructing the full tooth shape from the ready-to-use input, e.g., the partial intra-oral scan and the 2D panoramic image, is an applicable and valuable solution. In this paper, we propose a neural network, called ToothInpaintor, that takes as input a partial 3D dental model and a 2D panoramic image and reconstructs the full tooth model with high-quality root(s). Technically, we utilize the implicit rep- resentation for both the 3D and 2D inputs, and learn a latent space of the full tooth shapes. At test time, given an input, we successfully project it to the learned latent space via neural optimization to obtain the full tooth model conditioned on the input. To help find the robust projec- tion, a novel adversarial learning module is exploited in our pipeline. We extensively evaluate our method on a dataset collected from real-world clinics. The evaluation, comparison, and comprehensive ablation studies demonstrate that our approach