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VideoVAEPlus Tactile

A visuo-tactile finetune of VideoVAE+ (the sota-4-16z, 16-latent-channel, text-free variant). Starting from the released sota-4-16z.ckpt, the autoencoder was finetuned to encode/decode tactile sensor video...

KindVAEBase modelVideoVerses/VideoVAEPlusVersionvggBgrade

Model source

Kind
VAE
File size
7.12 KB
Base model
VideoVerses/VideoVAEPlus
Version
vvgg
Updated
Oct 9, 2026
Source
Hugging Face

Source description

VideoVAE+ 16z — Tactile Finetune

A visuo-tactile finetune of VideoVAE+ (the sota-4-16z, 16-latent-channel, text-free variant). Starting from the released sota-4-16z.ckpt, the autoencoder was finetuned to encode/decode tactile sensor video (left/right tactile streams) alongside RGB view frames.

Model

  • Architecture: AutoencoderKL2plus1D_1dcnn (factorized 2+1D KL autoencoder, 1D temporal CNN)
  • Latent channels (z_channels): 16
  • Spatial compression: 8× (ch_mult=[1,2,4,4], 3 downsamples)
  • Temporal compression: 4× (16 frames → 4 latent timesteps)
  • Text conditioning: none (caption_guide: False)
  • Base checkpoint: sota-4-16z.ckpt from VideoVerses/VideoVAEPlus
  • Finetune objective: LPIPSWithDiscriminator3D (KL weight 1e-6, disc weight 0.5), base LR 5e-5

Files

  • videovae_plus_16z_tactile.ckpt — final finetuned weights (Lightning checkpoint, ~5 GB).
  • Source code, configs, and inference scripts mirrored from the working repo.

Usage

# reconstruct a video with the finetuned autoencoder
python inference_video.py \
    --config configs/inference/config_16z_infer_noloss.yaml \
    --ckpt   videovae_plus_16z_tactile.ckpt \
    --input  examples/videos/gt/0510_episode_000_tactile_left.mp4

See the included configs/train/config_16z_tactile.yaml for the exact finetuning recipe. Load the checkpoint into the AutoencoderKL2plus1D_1dcnn model defined in src/models/autoencoder2plus1d_1dcnn.py.

License & attribution

Code and base model from VideoVerses/VideoVAEPlus (Apache-2.0). This repository adds tactile-finetuned weights and the corresponding training/inference configs.

Sources

1 source
Hugging FacePrimaryVersion a4aeea2bc77ecc165fdc8c36abe9a5eeeea8484d
huggingface.co/WitneyWW/VideoVAEPlus-tactile/tree/a4aeea2bc77ecc165fdc8c36abe9a5eeeea8484d

Verified Oct 9

Model artifacts

1 artifact

taming/modules/autoencoder/lpips/vgg.pth

pth · 7.12 KB · SHA-256 a78928a0af1e…2868 · Hugging Face

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Source excerpts

2 excerpts

--- license: apache-2.0 tags: - video - vae - autoencoder - tactile - visuo-tactile - world-model library_name: pytorch base_model: VideoVerses/VideoVAEPlus pipeline_tag: video-to-video --- # VideoVAE+ 16z — Tactile Finetune A visuo-tactile finetune of [VideoVAE+](https://github.com/VideoVerses/VideoVAEPlus) (the `sota-4-16z`, 16-latent-channel, text-free variant). Starting from the released `sota-4-16z.ckpt`, the autoencoder was finetuned to encode/decode tactile sensor video (left/right tactile streams) alongside RGB view frames. ## Model - **Architecture:** `AutoencoderKL2plus1D_1dcnn` (factorized 2+1D KL autoencoder, 1D temporal CNN) - **Latent channels (`z_channels`):** 16 - **Spatial compression:** 8× (`ch_mult=[1,2,4,4]`, 3 downsamples) - **Temporal compression:** 4× (16 frames → 4 latent timesteps) - **Text conditioning:** none (`caption_guide: False`) - **Base checkpoint:** `sota-4-16z.ckpt` from VideoVerses/VideoVAEPlus - **Finetune objective:** `LPIPSWithDiscriminator3D` (KL weight 1e-6, disc weight 0.5), base LR 5e-5 ## Files - `videovae_plus_16z_tactile.ckpt` — final finetuned weights (Lightning checkpoint, ~5 GB). - Source code, configs, and inference scripts mirrored from the working repo. ## Usage ```bash # reconstruct a video with the finetuned autoencoder python inference_video.py \ --config configs/inference/config_16z_infer_noloss.yaml \ --ckpt videovae_plus_16z_tactile.ckpt \ --input examples/videos/gt/0510_episode_000_tactile_left.mp4 ``` See the included `configs/train/config_16z_tactile.yaml` for the exact finetuning recipe. Load the checkpoint into the `AutoencoderKL2plus1D_1dcnn` model defined in `src/models/autoencoder2plus1d_1dcnn.py`. ## License & attribution Code and base model from [VideoVerses/VideoVAEPlus](https://github.com/VideoVerses/VideoVAEPlus) (Apache-2.0). This repository adds tactile-finetuned weights and the corresponding training/inference configs.

Source context: 0 downloads · 0 likes · Pipeline video-to-video · Library pytorch · Repo WitneyWW/VideoVAEPlus-tactile