DiViNE - Distilling Visual Neural Encoders

Model

Disconnected

Session: -

Producer: -

Anchor: none

Model: -

Protocol: 1.0

Ping RTT: -

TX - · BIN - · ACK - · RX -

Use this computer camera

Use a phone camera

Manual code

Input views

Waiting for View 1 before inference can start

Waiting for a camera

No source

Waiting for a required source

Waiting for a camera

No source

Available

Add a phone camera

Scan once per phone. Each phone fills the next available view.

Manual code

2 phone slots available

FPS: 0 • Anny: 0 • VPS: 0 in • 0 out

Conf -

Conf 0.00 Labels

Solid: client-smoothed Wireframe: raw server

Opacity -

Reconstruction

Inference

Maximum vertices - Confidence - Pixel stride - Voxel size -

Human mesh output

Mesh voxel size - Mesh triangles -

Viewer

Point size 1.6

Performance mode Render on demand Preserve camera

PANSt3R overlay

Overlay opacity -

Anny

GPU skinning

Identity

Distinct person colours Person labels

Tracking

Maximum humans 5Confirm frames 10Coast frames 15

Association gate 3

Smoothing

Enabled

Kalman 1-euro pose Phenotype Delta clamp Joint limits Ground contact Temporal consistency

Tuning

Process noise 1.5Measurement noise 0.15Pose min cutoff 1.5Pose beta 0.5Pose d-cutoff 1Phenotype alpha 0.4Phenotype max delta/s 5

Motion

Interpolate between server frames

Chase rate 15

FPS: 0

Overlay settings

Overlay alpha: 0.50

FPS: 0

FPS: 0

cam 0 · send 0

Main FPS: 0

Anchor FPS: 0

0 0

Ask about the image

Full Answer

Error

Keyboard shortcuts

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Console

Filter PAUSED

Health: - Proto: - Anchor Seq: - Last Server Seq: - FPS 10 Opacity 0.5

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Diagnostics

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Configure

Anchor

VQA questions

Format

Export outputs

Depth Semseg Humans VQA text Point clouds Input frames

Fewer outputs = faster export

Presets

History

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DiViNE demo client for real‑time multi‑modal visual understanding & 3D reconstruction over the DiViNE WebSocket protocol. Internal research & evaluation use only; provided “as is”. DiViNE continues prior efforts UNIC (Universal Classification Models via Multi‑teacher Distillation) and DUNE (Distilling a UNiversal Encoder). Maintained by Mert Bulent Sariyildiz, Philippe Weinzaepfel, Thomas Lucas, Pau De Jorge, Diane Larlus, Yannis Kalantidis, César De Souza, and Martin Humenberger. Project page