Signvrse/signvrse-momask-rvqvae

Signvrse MoMask Motion RVQ-VAE

MoMask-style residual vector-quantized VAE trained on Signvrse motion features.

Model

Field	Value
Training step	200,000
Input dim	668
Latent dim	512
Codebooks	6 × 512
Window length	64 frames
Downsample factor	4

Files

• model.safetensors — model weights only (for inference)
• checkpoint.pt — full training checkpoint (optimizer + scheduler + args)
• config.json — architecture hyperparameters
• training_metadata.json — training step and CLI args

Loading

import json
import torch
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download
from rvqvae import MotionResidualRVQVAE

repo_id = "Signvrse/signvrse-momask-rvqvae"
config_path = hf_hub_download(repo_id, "config.json")
weights_path = hf_hub_download(repo_id, "model.safetensors")

with open(config_path) as f:
    cfg = json.load(f)

model = MotionResidualRVQVAE(
    input_dim=cfg["input_dim"],
    latent_dim=cfg["latent_dim"],
    num_codebooks=cfg["num_codebooks"],
    codebook_size=cfg["codebook_size"],
    quant_dropout_prob=cfg["quant_dropout_prob"],
    ema_decay=cfg["ema_decay"],
)
model.load_state_dict(load_file(weights_path))
model.eval()

# x: [batch, 668, seq_len] float32 motion features
# out = model(x)

Install the model code from this repository's rvqvae package, or copy rvqvae/momask_rvqvae.py into your project.