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neural velocity field

A neural velocity field is a time-dependent continuous vector field parameterized by an artificial neural network that specifies the instantaneous rate and direction of movement for points within a spatial coordinate or state space. In continuous generative modeling paradigms such as flow matching and continuous normalizing flows, the neural network is trained to predict these velocity vectors to govern how probability distributions evolve over continuous time. By integrating the learned velocity field using ordinary differential equations, the system continuously transports unstructured samples, such as random noise distributions, along defined trajectories into complex structured data distributions, such as three-dimensional point clouds or images. Constraining or straightening these learned continuous paths allows models to generate high-fidelity geometric and generative outputs with fewer numerical integration steps.

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Fast Point Cloud Generation with Straight Flows

Fast Point Cloud Generation with Straight Flows

Lemeng Wu, Dilin Wang, Chengyue Gong, Xingchao Liu, Yunyang Xiong, Rakesh Ranjan, Raghuraman Krishnamoorthi, Vikas Chandra, Qiang Liu

OrganizationsMetaUniversity of Texas at Austin

Why you should read this

Proposes Point Straight Flow, a novel framework that straightens generative transport trajectories and distills them into a single step, enabling high-quality 3D point cloud generation over 700 times faster than standard diffusion models.

Diffusion models have emerged as a powerful tool for point cloud generation. A key component that drives the impressive performance for generating high-quality samples from noise is iteratively denoise for thousands of steps. While beneficial, the complexity of learning steps has limited its applications to many 3D real-world. To address this limitation, we propose Point Straight Flow (PSF), a model that exhibits impressive performance using one step. Our idea is based on the reformulation of the standard diffusion model, which optimizes the curvy learning trajectory into a straight path. Further, we develop a distillation strategy to shorten the straight path into one step without a performance loss, enabling applications to 3D real-world with latency constraints. We perform evaluations on multiple 3D tasks and find that our PSF performs comparably to the standard diffusion model, outperforming other efficient 3D point cloud generation methods. On real-world applications such as point cloud completion and training-free text-guided generation in a low-latency setup, PSF performs favorably.

Added

2026-09-26