This article aims at unpaired shape-to-shape transformation for 3D point clouds, for instance, turning a chair to its table counterpart. Recent work for 3D shape transfer or deformation highly relies on paired inputs or specific correspondences. However, it is usually not feasible to assign precise correspondences or prepare paired data from two domains. A few methods start to study unpaired learning, but the characteristics of a source model may not be preserved after transformation. To overcome the difficulty of unpaired learning for transformation, we propose alternately training the autoencoder and translators to construct shape-aware latent space. This latent space based on novel loss functions enables our translators to transform 3D point clouds across domains and maintain the consistency of shape characteristics. We also crafted a test dataset to objectively evaluate the performance of point-cloud translation. The experiments demonstrate that our framework can construct high-quality models and retain more shape characteristics during cross-domain translation compared to the state-of-the-art methods. Moreover, we also present shape editing applications with our proposed latent space, including shape-style mixing and shape-type shifting, which do not require retraining a model.

Stories are diverse and highly personalized, resulting in a large possible output space for story generation. Existing end-to-end approaches produce monotonous stories because they are limited to the vocabulary and knowledge in a single training dataset. This paper introduces KG-Story, a three-stage framework that allows the story generation model to take advantage of external Knowledge Graphs to produce interesting stories. KG-Story distills a set of representative words from the input prompts, enriches the word set by using external knowledge graphs, and finally generates stories based on the enriched word set. This distill-enrich-generate framework allows the use of external resources not only for the enrichment phase, but also for the distillation and generation phases. In this paper, we show the superiority of KG-Story for visual storytelling, where the input prompt is a sequence of five photos and the output is a short story. Per the human ranking evaluation, stories generated by KG-Story are on average ranked better than that of the state-of-the-art systems. Our code and output stories are available at https://github.com/zychen423/KE-VIST.