PointInfinity: Resolution-Invariant Point Diffusion Models
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.
660f66749f61fdeb2deacb3c
Zixuan Huang
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2024-04-05T10:14:36.314Z
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Zixuan Huang
user
zxhuang1698
660f66749f61fdeb2deacb3d
Justin Johnson
660f66749f61fdeb2deacb3e
Shoubhik Debnath
660f66749f61fdeb2deacb3f
James M. Rehg
660f66749f61fdeb2deacb40
Chao-Yuan Wu
PointInfinity: Resolution-Invariant Point Diffusion Models
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.
PointInfinity, a transformer-based point cloud diffusion model, efficiently generates high-resolution point clouds with state-of-the-art quality by using a fixed-size, resolution-invariant latent representation.
point cloud diffusion models transformer-based architecture resolution-invariant latent representation classifier-free guidance CO3D point clouds high-resolution state-of-the-art quality