EntVista/metrixel-character-renders

Metrixel Animated Character Renders

Multi-view renders, signed-distance-field volumes and per-view mesh tensors produced by Metrixel from a small set of rigged, animated humanoid characters.

Each character is captured from four camera angles (0°, 90°, 180°, 270°) across ~30 sampled frames of its motion clip, at 512×512. Every frame/angle carries a matching 64³ signed-distance-field volume and a per-view mesh tensor, so the geometry, the implicit surface and the image are aligned sample-for-sample.

This is the output of a pipeline run, not the input to one: the source character models are not redistributed here. The point is to show what a Metrixel run produces — same command, different assets.

Want these outputs from your own models? Sign up free — 1,000 export tokens, no credit card.

Worth knowing before you invest time: a free account covers single-file runs: turntable and multi-view renders exported as PNG or JPG (wireframe included), texture extraction, and .pt mesh tensor export.

The two things that produced this set are batch mode (a whole folder in one command) and SDF volume export. Those are Professional, along with 16-bit TIFF and Motion Retargeting — US$59/month, or US$590/year.

Running this across a real asset library? Open a discussion on this dataset with your input format and the outputs you need, and we can arrange Professional evaluation access for that workflow.

This is a feature showcase / sample, not a full training corpus. It exists to demonstrate Metrixel's batch pipeline and on-disk schema.

Generated output only. The source models are not redistributed here — see the provenance table for where each one came from.

Preview

What's inside

data/<asset_id>/
├── images/      512x512 RGBA renders, one per camera angle per frame (transparent background)
├── sdf/         signed-distance volumes — raw little-endian float32, 64³ grid, row-major (z,y,x)
├── meshes/      per-view mesh tensors (.pt: vertices + normals + UVs + faces)
├── animations/  rig take in USD (.usda)
└── textures/    embedded material textures (.jpg)
metadata.jsonl   one row per asset: paths + license + geometry stats

Image filenames encode the camera + frame as AZ_AZ_AZ_FRAME (3-digit azimuth, 4-digit frame). SDF files are sdf_<az>_<el>_<roll>_<frame>.bin; load with numpy.fromfile(path, dtype='<f4').reshape(R, R, R) (negative = inside, positive = outside, zero on the surface).

Loading & visualizing

Open-source tooling for the .pt mesh tensors and the raw SDF volumes lives in metrixel-3d-dataset-tools (MIT):

git clone https://github.com/entvistastudio/metrixel-3d-dataset-tools
# mesh_visualization/ — load + view the per-view .pt tensors
# sdf_visualization/  — inspect the float32 SDF volumes

How it was generated

Metrixel 2026.3.0, CLI (preview — pin the version for reproducibility). SDF backend actually used: vulkan.

metrixel --mode cli --gen_mode batch \
  --input <animated-characters>/ --output <out>/ \
  --width 512 --height 512 \
  --single_frame=false --camera_step=90 \
  --frames_per_second=30 --frame_stride=50 \
  --animation_stay_in_place=true \
  --frame_format=png \
  --camera_distance_factor=1.4 \
  --gen_sdf=true --sdf_format=bin --sdf_backend=auto \
  --manifest=true

Provenance

asset	source	licence	tris	generated
AncientGeneral.glb	generated	evs-sample-assets-1.0	59999	2026-08-10
AncientKing.glb	generated	evs-sample-assets-1.0	60000	2026-08-10
AncientOfficer.glb	generated	evs-sample-assets-1.0	59999	2026-08-10
AncientRankedOfficer.glb	generated	evs-sample-assets-1.0	60000	2026-08-10
AncientSolider.glb	generated	evs-sample-assets-1.0	59999	2026-08-10

Licensing

Released under the EVS Sample Assets Licence 1.0 (evs-sample-assets-1.0). You may use, modify, redistribute and sell work made with these outputs, in commercial projects, with no attribution required.

One category of use is excluded: this material may not be used as training, fine-tuning or validation data for generative 3D models (text-to-3D, image-to-3D, mesh generation), nor included in datasets assembled for that purpose. Analytical and computer-vision research, benchmarking of existing tools, and reconstruction or segmentation work are all expressly permitted — see Clause 2.

The underlying characters were produced with generative tooling and the motion is retargeted from a separately-sourced capture. Everything here is provided as is, with no warranty of uniqueness, originality or non-infringement.

Honest limits

• Renders are consistent, not cinematic: fixed lighting, transparent background, four fixed angles at 512×512.
• Four camera angles only. This is enough to show view consistency; it is not dense multi-view coverage.
• The source geometry is clean and watertight, so the SDF path here never exercises the generalized-winding-number route that non-watertight real-world scans require. Do not read this set as evidence for that case.
• Every character performs the same motion clip, so the variation across the set is in the characters, not the motion.
• Small set, single class (humanoid characters). Not a benchmark and not a corpus.

Related

• Open-source readers for the .pt mesh tensors and raw SDF volumes: metrixel-3d-dataset-tools (MIT).
• The source characters these were rendered from are published separately as metrixel-rigged-characters — rigged, animated GLBs rather than pipeline output.