flow-matching

This paper proposes Missing-Data Flow Matching, a method that treats missing coordinates of training samples as latent variables and averages the flow matching loss over possible values. Theoretical analysis shows the correction is exact and provides design guidance, with experiments validating the approach on tabular data.

AURORA-LM introduces a continuous-latent diffusion language model that separates decodable text representation from distribution modeling, achieving strong performance on OpenWebText and XSum while scaling to 1B parameters.

MiniWorld is a reproducible framework for training video world models from scratch using a block-causal Video Diffusion Transformer with Flow Matching, enabling efficient streaming generation and trainable in days on a single 8-GPU server.

DreamTraj predicts 6-DoF object trajectories from a single RGB image and a language instruction by decoding internal video diffusion latents, eliminating the need for video, depth, or CAD models at inference. It introduces the MOVEdataset with fine-grained language-to-motion annotations and achieves state-of-the-art performance while running 4.6x faster than generate-then-extract pipelines.

The paper introduces Latent-Kernel Discrete Flow Maps (LKF), a flow-map kernel for discrete diffusion models that captures correlations between positions via a shared latent, enabling few-step generation without distillation and improving text generation perplexity.

SE(3)-MeanFlow introduces a few-step generative framework for protein backbone generation on Lie groups, extending MeanFlow to SE(3) with closed-form average-velocity training targets and a rectification-based post-training that matches or exceeds flow-matching baselines at reduced sampling steps.

This paper introduces PlatformBid, the first comprehensive auto-bidding benchmark designed from a unified advertising platform perspective, along with BidFlow, a novel flow-matching-based auto-bidding method. Experiments show BidFlow improves target cost by +0.68% in online tests on Kuaishou.

FMOPF uses latent flow matching with constraint-aware interaction priors to generate diverse, feasible near-optimal solutions for AC optimal power flow, scaling to hundreds of buses while preserving feasibility.

Meshy T2 introduces a fast native mesh generation framework using flow matching and a vertex-set mesh VAE, achieving state-of-the-art geometric fidelity with end-to-end image-to-mesh generation in a median of 6 seconds, over an order of magnitude faster than autoregressive baselines.

Parallel Decoding Distillation (PDD) is a trajectory-based distillation method that accelerates image and video generation by predicting multiple denoising steps per network evaluation, achieving state-of-the-art performance with 4-8 NFEs on models like LTX-2.3, Wan14B, and Qwen-Image.

The paper introduces a dependency-aware fidelity diagnostic to measure inter-column dependency in synthetic tabular data, revealing that standard metrics are blind to dependency and that current generators have a residual gap not closed by capacity increases or common fixes.

Released a model (Stereo2Spatial) that converts stereo music tracks to spatialized binaural mixes, using flow-matching diffusion and amplitude lifting for stable training. The model and a Windows app are open-sourced under Apache 2.0.

FreyaTTS is a compact, tokenizer-free Turkish-first text-to-speech model based on a non-autoregressive conditional flow-matching Diffusion Transformer, achieving state-of-the-art performance with a fraction of the parameters of larger systems and released under Apache-2.0.

This paper introduces Reward Transport, a method that uses optimal transport coupling during flow matching training to align a scalar noise coordinate with molecular rewards, enabling monotone control over molecular properties like logP and QED at inference without additional computation.

This paper introduces Truncated Jump Sampling (TJS), a training-free method that accelerates diffusion and flow matching model generation by exploiting endpoint decodability, reducing neural function evaluations by 20–70% with near-matched quality across multiple models.

Flow-ERD is a multi-agent traffic simulator that combines agent-type aware flow matching with entropy-regularized distillation to achieve both realistic and diverse motion patterns, ranking first on the WOSAC test benchmark.

The author shares their experience building a small flow matching image generation model trained on Apple emoji images, describing the initial failed approach and the successful pivot using RGB channels, residual blocks, and attention.

Perceptual Flow Matching supervises flow matching in perceptual feature space, enabling high-quality few-step generation with 4-8 sampling steps instead of 35-50, without needing teacher models.

Proposes Quantum Flow Matching (QFM), a generative model that uses spin Wigner functions and functional flow matching to learn and generate multi-qubit quantum distributions, accurately capturing physical properties like purity and entanglement entropy.

Proposes SNAP-FM, a method that leverages sparse GPU nonlinear optimization to accelerate constraint projection in physics-constrained generative modeling, achieving faster inference while preserving exact physical constraint satisfaction.