PointInfinity: Resolution-Invariant Point Diffusion Models
Zixuan Huang Justin Johnson Shoubhik Debnath James M. Rehg Chao-Yuan Wu
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.