title: PointInfinity: Resolution-Invariant Point Diffusion Models
summary: We present PointInfinity, an efficient family of point cloud diffusion
  models. Our core idea is to use a transformer-based architecture with a
  fixed-size, resolution-invariant latent representation. This enables efficient
  training with low-resolution point clouds, while allowing high-resolution point
  clouds to be generated during inference. More importantly, we show that scaling
  the test-time resolution beyond the training resolution improves the fidelity
  of generated point clouds and surfaces. We analyze this phenomenon and draw a
  link to classifier-free guidance commonly used in diffusion models,
  demonstrating that both allow trading off fidelity and variability during
  inference. Experiments on CO3D show that PointInfinity can efficiently generate
  high-resolution point clouds (up to 131k points, 31 times more than Point-E)
  with state-of-the-art quality.
paper_authors._id: 660f66749f61fdeb2deacb3c
paper_authors.name: Zixuan Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-05T10:14:36.314Z
paper_authors._id: 629fe0dd198a9a5a755f3ce0
paper_authors.avatarUrl: /avatars/e643d43f66c10729f155edca96aef1f8.svg
paper_authors.fullname: Zixuan Huang
paper_authors.type: user
paper_authors.user: zxhuang1698
paper_authors._id: 660f66749f61fdeb2deacb3d
paper_authors.name: Justin Johnson
paper_authors._id: 660f66749f61fdeb2deacb3e
paper_authors.name: Shoubhik Debnath
paper_authors._id: 660f66749f61fdeb2deacb3f
paper_authors.name: James M. Rehg
paper_authors._id: 660f66749f61fdeb2deacb40
paper_authors.name: Chao-Yuan Wu
paper_title: PointInfinity: Resolution-Invariant Point Diffusion Models
paper_summary: We present PointInfinity, an efficient family of point cloud diffusion
  models. Our core idea is to use a transformer-based architecture with a
  fixed-size, resolution-invariant latent representation. This enables efficient
  training with low-resolution point clouds, while allowing high-resolution point
  clouds to be generated during inference. More importantly, we show that scaling
  the test-time resolution beyond the training resolution improves the fidelity
  of generated point clouds and surfaces. We analyze this phenomenon and draw a
  link to classifier-free guidance commonly used in diffusion models,
  demonstrating that both allow trading off fidelity and variability during
  inference. Experiments on CO3D show that PointInfinity can efficiently generate
  high-resolution point clouds (up to 131k points, 31 times more than Point-E)
  with state-of-the-art quality.
paper_ai_keywords: point cloud diffusion models transformer-based architecture resolution-invariant latent representation classifier-free guidance