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Textures are a vital aspect of creating visually appealing and realistic 3D models. In this paper, we study the problem of generating high-fidelity texture given shapes of 3D assets, which has been relatively less explored compared with generic 3D shape modeling. Our goal is to facilitate a controllable texture generation process, such that one texture code can correspond to a particular appearance style independent of any input shapes from a category. We introduce Texture UV Radiance Fields (TUVF) that generate textures in a learnable UV sphere space rather than directly on the 3D shape. This allows the texture to be disentangled from the underlying shape and transferable to other shapes that share the same UV space, i.e., from the same category. We integrate the UV sphere space with the radiance field, which provides a more efficient and accurate representation of textures than traditional texture maps. We perform our experiments on real-world object datasets where we achieve not only realistic synthesis, but also substantial improvements over state-of-the-arts on texture controlling and editing.

@inproceedings{cheng2024tuvf,
  title     = {TUVF: Learning Generalizable Texture UV Radiance Fields},
  author    = {Cheng, An-Chieh and Li, Xueting and Liu, Sifei and Wang, Xiaolong},
  booktitle = {International Conference on Learning Representations}
  year      = {2024}
}
                

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With the capacity of modeling long-range dependencies in sequential data, transformers have shown remarkable performances in a variety of generative tasks such as image, audio, and text generation. Yet, taming them in generating less structured and voluminous data formats such as high-resolution point clouds have seldom been explored due to ambiguous sequentialization processes and infeasible computation burden. In this paper, we aim to further exploit the power of transformers and employ them for the task of 3D point cloud generation. The key idea is to decompose point clouds of one category into semantically aligned sequences of shape compositions, via a learned canonical space. These shape compositions can then be quantized and used to learn a context-rich composition codebook for point cloud generation. Experimental results on point cloud reconstruction and unconditional generation show that our model performs favorably against state-of-the-art approaches. Furthermore, our model can be easily extended to multi-modal shape completion as an application for conditional shape generation.

@inproceedings{cheng2022autoregressive,
  title     = {Learning 3D Dense Correspondence via Canonical Point Autoencoder},
  author    = {Cheng, An-Chieh and Li, Xueting and Liu, Sifei and Sun, Min and Yang, Ming-Hsuan},
  booktitle = {ECCV}
  year      = {2022}
}
                

pdf | website | code | abstract | bibtex

We propose a canonical point autoencoder (CPAE) that predicts dense correspondences between 3D shapes of the same category. The autoencoder performs two key functions: (a) encoding an arbitrarily ordered point cloud to a canonical primitive, e.g., a sphere, and (b) decoding the primitive back to the original input instance shape. As being placed in the bottleneck, this primitive plays a key role to map all the unordered point clouds on the canonical surface and to be reconstructed in an ordered fashion. Once trained, points from different shape instances that are mapped to the same locations on the primitive surface are determined to be a pair of correspondence. Our method does not require any form of annotation or self-supervised part segmentation network and can handle unaligned input point clouds. Experimental results on 3D semantic keypoint transfer and part segmentation transfer show that our model performs favorably against state-of-the-art correspondence learning methods.

@inproceedings{cheng2021learning,
  title     = {Learning 3D Dense Correspondence via Canonical Point Autoencoder},
  author    = {Cheng, An-Chieh and Li, Xueting and Sun, Min and Yang, Ming-Hsuan and Liu, Sifei},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2021}
}