3 research outputs found
Discrete Point Flow Networks for Efficient Point Cloud Generation
Generative models have proven effective at modeling 3D shapes and their
statistical variations. In this paper we investigate their application to point
clouds, a 3D shape representation widely used in computer vision for which,
however, only few generative models have yet been proposed. We introduce a
latent variable model that builds on normalizing flows with affine coupling
layers to generate 3D point clouds of an arbitrary size given a latent shape
representation. To evaluate its benefits for shape modeling we apply this model
for generation, autoencoding, and single-view shape reconstruction tasks. We
improve over recent GAN-based models in terms of most metrics that assess
generation and autoencoding. Compared to recent work based on continuous flows,
our model offers a significant speedup in both training and inference times for
similar or better performance. For single-view shape reconstruction we also
obtain results on par with state-of-the-art voxel, point cloud, and mesh-based
methods.Comment: In ECCV'2
AI-generated Content for Various Data Modalities: A Survey
AI-generated content (AIGC) methods aim to produce text, images, videos, 3D
assets, and other media using AI algorithms. Due to its wide range of
applications and the demonstrated potential of recent works, AIGC developments
have been attracting lots of attention recently, and AIGC methods have been
developed for various data modalities, such as image, video, text, 3D shape (as
voxels, point clouds, meshes, and neural implicit fields), 3D scene, 3D human
avatar (body and head), 3D motion, and audio -- each presenting different
characteristics and challenges. Furthermore, there have also been many
significant developments in cross-modality AIGC methods, where generative
methods can receive conditioning input in one modality and produce outputs in
another. Examples include going from various modalities to image, video, 3D
shape, 3D scene, 3D avatar (body and head), 3D motion (skeleton and avatar),
and audio modalities. In this paper, we provide a comprehensive review of AIGC
methods across different data modalities, including both single-modality and
cross-modality methods, highlighting the various challenges, representative
works, and recent technical directions in each setting. We also survey the
representative datasets throughout the modalities, and present comparative
results for various modalities. Moreover, we also discuss the challenges and
potential future research directions