A viser extension with out-of-the-box support for the time dimension
Project description
viser4d
viser4d is a small wrapper around viser that adds a time dimension. It records
scene operations across timesteps, supports timeline-synced audio playback, and
can seek or play them back.
Quickstart
pip install viser4d
import numpy as np
import viser4d
server = viser4d.Viser4dServer(num_steps=10)
with server.at(0):
points = np.random.uniform(-1.0, 1.0, size=(200, 3))
point_cloud = server.scene.add_point_cloud(
"/points",
points=points,
colors=(255, 200, 0),
)
for i in range(1, 10):
with server.at(i):
points = np.random.uniform(-1.0, 1.0, size=(200, 3))
point_cloud.points = points
server.play(fps=10, loop=True)
server.sleep_forever()
Streaming ingest
If data arrives incrementally, initialize components at t=0 and then record
updates as each new frame arrives:
import numpy as np
import viser4d
num_steps = 180
server = viser4d.Viser4dServer(num_steps=num_steps)
def get_next_points() -> np.ndarray:
# Replace with your real sensor/network/pipeline frame source.
return np.random.normal(size=(400, 3)).astype(np.float32)
with server.at(0):
point_cloud = server.scene.add_point_cloud(
"/stream/points",
points=get_next_points(),
)
for t in range(1, num_steps):
points = get_next_points()
with server.at(t):
point_cloud.points = points
server.seek(t) # optional: keep view synced to latest streamed frame
server.play(fps=30, loop=True)
server.sleep_forever()
Timestep callbacks
If you have your own visualization logic and just want to use viser4d's timeline infrastructure (playback controls, seeking, scrubbing), you can register a callback that fires whenever the timestep changes:
import viser4d
server = viser4d.Viser4dServer(num_steps=100)
def on_timestep(t: int) -> None:
# Update your custom visualizations here
update_video_frames(t)
update_body_meshes(t)
update_3d_keypoints(t)
server.on_timestep_change(on_timestep)
server.play(fps=30, loop=True)
server.sleep_forever()
Callbacks are invoked after viser4d applies its own recorded state, so you can
mix both approaches - record some operations with at(t) and handle others via
callbacks.
Serialize .viser recordings
To serialize the full viser4d timeline, including audio, use server.serialize():
import viser4d
server = viser4d.Viser4dServer(num_steps=100)
# ... record timeline data ...
blob = server.serialize(start_timestep=0, end_timestep=None)
Write the returned bytes to disk yourself if needed.
Streaming audio append
For audio that arrives incrementally, create a track once inside at(t) and
append chunks through the returned handle:
import numpy as np
import viser4d
server = viser4d.Viser4dServer(num_steps=300, fps=30)
with server.at(0):
audio = server.audio.add_track(
"/stream/audio",
data=np.zeros(1600, dtype=np.float32),
sample_rate=16000,
)
for _ in range(120):
chunk = np.random.uniform(-0.05, 0.05, size=(1600,)).astype(np.float32)
audio.append(chunk)
AudioHandle.append(...) extends the same track contiguously (same channel
count).
How it works
Context determines behavior. server.scene is viser's normal scene API, but
its websocket target is swapped while you're inside an at(t) context:
Inside at(t): Outside at(t):
───────────── ──────────────
scene.add_frame(...) scene.add_frame(...)
│ │
▼ ▼
records to Timeline forwards to live viser scene
- Inside
at(t): Operations are recorded to a timeline, not executed. - Outside
at(t): Operations forward directly to viser's live scene. - Playback:
seek(t)orplay()applies recorded state to the live scene. - Audio: Add timeline-synced tracks with
server.audio.add_track(...).
See examples/ for more.
Quality checks
uvx ruff format .
uvx ruff check .
uvx ty check
pnpm run typecheck:runtime
pnpm run build:runtime
Tests
pnpm run build:runtime
uv run --group dev pytest -q
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