Distance Marching for Generative Modeling

arXiv:2602.02928v2 Announce Type: replace Abstract: Modern generative models based on diffusion or flow matching transport noise toward the data manifold, without representing the data manifold explicitly. We propose Distance Marching that explores an alternative design by explicitly modeling a distance field of the data manifold. A distance field offers a geometric view of generation as a descent process toward the data manifold using only the current position. The negative gradient of the distance field provides the direction, and the distance value can optionally determine the step size. However, sparse training data in high-dimensional space make it difficult to identify the closest points on the data manifold needed to train the distance field. To address this challenge, we design new loss functions that emphasize denoising targets from closer data points. Our analysis shows that, without time input, emphasizing closer targets reduces denoising ambiguity and the bias toward the data mean. This distance-field perspective naturally motivates two principled deterministic samplers, showing fast convergence in inference. As a result, our model is the only one without time input that remains competitive with time-conditioned models across datasets and architectures. Moreover, our distance prediction is also helpful for early stopping during sampling and for out-of-distribution detection. We hope distance-field modeling can open a promising direction for exploring generative modeling from a geometric perspective.

Abstract:Modern generative models based on diffusion or flow matching transport noise toward the data manifold, without representing the data manifold explicitly. We propose Distance Marching that explores an alternative design by explicitly modeling a distance field of the data manifold. A distance field offers a geometric view of generation as a descent process toward the data manifold using only the current position. The negative gradient of the distance field provides the direction, and the distance value can optionally determine the step size. However, sparse training data in high-dimensional space make it difficult to identify the closest points on the data manifold needed to train the distance field. To address this challenge, we design new loss functions that emphasize denoising targets from closer data points. Our analysis shows that, without time input, emphasizing closer targets reduces denoising ambiguity and the bias toward the data mean. This distance-field perspective naturally motivates two principled deterministic samplers, showing fast convergence in inference. As a result, our model is the only one without time input that remains competitive with time-conditioned models across datasets and architectures. Moreover, our distance prediction is also helpful for early stopping during sampling and for out-of-distribution detection. We hope distance-field modeling can open a promising direction for exploring generative modeling from a geometric perspective.

Machine Learning (cs.LG)

arXiv:2602.02928 [cs.LG]

(or arXiv:2602.02928v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2602.02928 Focus to learn more arXiv-issued DOI via DataCite

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Distance Marching for Generative Modeling

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