{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:06:13Z","timestamp":1764842773813},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>3D surface super-resolution is an important technical tool in virtual reality, and it is also a research hotspot in computer vision. Due to the unstructured and irregular nature of 3D object data, it is usually difficult to obtain high-quality surface details and geometry textures via a low-cost hardware setup. In this paper, we establish a multimodal-driven variational autoencoder (mmVAE) framework to perform 3D surface enhancement based on 2D normal images. To fully leverage the multimodal learning, we investigate a multimodal Gaussian mixture model (mmGMM) to align and fuse the latent feature representations from different modalities, and further propose a cross-scale encoder-decoder structure to reconstruct high-resolution normal images. Experimental results on several benchmark datasets demonstrate that our method delivers promising surface geometry structures and details in comparison with competitive advances.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/175","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T04:31:30Z","timestamp":1691728290000},"page":"1578-1586","source":"Crossref","is-referenced-by-count":2,"title":["3D Surface Super-resolution from Enhanced 2D Normal Images: A Multimodal-driven Variational AutoEncoder Approach"],"prefix":"10.24963","author":[{"given":"Wuyuan","family":"Xie","sequence":"first","affiliation":[{"name":"Shenzhen University, Guangdong Key Laboratory of Intelligent Information Processing"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tengcong","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen University, Guangdong Key Laboratory of Intelligent Information Processing"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miaohui","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen University, Guangdong Key Laboratory of Intelligent Information Processing;"},{"name":"State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University);"},{"name":"Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2023","name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","start":{"date-parts":[[2023,8,19]]},"theme":"Artificial Intelligence","location":"Macau, SAR China","end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T04:39:43Z","timestamp":1691728783000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/175"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/175","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}