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AmandineBtto/
AGREE

Name: AGREE
Availability: InStock

pytorch

audio-visual-learning

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AGREE

Official model weights for AGREE, introduced in the paper "Few-shot Acoustic Synthesis with Multimodal Flow Matching" by Amandine Brunetto (CVPR 2026).

AGREE is a joint embedding model for acoustics and room geometry. It learns a shared representation between room impulse responses (RIRs) and panoramic depth maps captured at the receiver position.

The model can be used for:

Evaluating geometry consistency of generated RIRs (via retrieval metrics and Fréchet distance, as done in the paper)
Downstream multimodal learning tasks involving acoustics and geometry
Audio-visual representation learning

This repository contains the pretrained weights. To run AGREE, please use the official codebase.

Available checkpoints

file	description
`AGREE_AR.ckpt`	Model trained on the Acoustic Rooms (AR) training set. Intended for downstream tasks.
`AGREE_fullAR.ckpt`	Model trained on the full AR dataset. Used in the paper for evaluation of RIR generation.
`AGREE_fullHAA.ckpt`	Model fine-tuned on the full HAA dataset, used for evaluation of RIR generation.

Download

Weights can be downloaded with:

huggingface-cli download AmandineBtto/AGREE --local-dir weights/AGREE