Our paper on texture generation using dual-domain feature flow with multi-view hallucination is accepted to AAAI 2022

[2021.12.02]

The following paper is accepted to the AAAI Conference on Artificial Intelligence (AAAI 2022):

  • Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucination by Seunggyu Chang, Jungchan Cho, and Songhwai Oh 
    • Abstract: We propose a dual-domain generative model to estimate a texture map from a single image for colorizing a 3D human model. When estimating a texture map, a single image is insufficient as it reveals only one facet of a 3D object. To provide sufficient information for estimating a complete texture map, the proposed model simultaneously generates multi-view hallucinations in the image domain when estimating a texture map in the texture domain. During the generating process, each domain generator exchanges features to the other by a flow-based local attention mechanism. In this manner, the proposed model can estimate a texture map utilizing abundant multi-view image features from which multi-view hallucinations are generated. As a result, the estimated texture map contains consistent colors and patterns over the entire region. Experiments show the superiority of our model for estimating a directly render-able texture map, which is applicable to 3D animation rendering. Furthermore, our model also improves an overall generation quality in the image domain for pose and viewpoint transfer tasks.