Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction


  • Ta-Ying Cheng Academia Sinica University of Oxford
  • Hsuan-Ru Yang Academia Sinica
  • Niki Trigoni University of Oxford
  • Hwann-Tzong Chen National Tsing Hua University
  • Tyng-Luh Liu Academia Sinica




Computer Vision (CV)


We present a pose adaptive few-shot learning procedure and a two-stage data interpolation regularization, termed Pose Adaptive Dual Mixup (PADMix), for single-image 3D reconstruction. While augmentations via interpolating feature-label pairs are effective in classification tasks, they fall short in shape predictions potentially due to inconsistencies between interpolated products of two images and volumes when rendering viewpoints are unknown. PADMix targets this issue with two sets of mixup procedures performed sequentially. We first perform an input mixup which, combined with a pose adaptive learning procedure, is helpful in learning 2D feature extraction and pose adaptive latent encoding. The stagewise training allows us to build upon the pose invariant representations to perform a follow-up latent mixup under one-to-one correspondences between features and ground-truth volumes. PADMix significantly outperforms previous literature on few-shot settings over the ShapeNet dataset and sets new benchmarks on the more challenging real-world Pix3D dataset.




How to Cite

Cheng, T.-Y., Yang, H.-R., Trigoni, N., Chen, H.-T., & Liu, T.-L. (2022). Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction. Proceedings of the AAAI Conference on Artificial Intelligence, 36(1), 427-435. https://doi.org/10.1609/aaai.v36i1.19920



AAAI Technical Track on Computer Vision I