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id string | task string | messages list |
|---|---|---|
00000054c36d44a2a483bdbff31d8edf | text_to_mesh | [
{
"role": "user",
"content": "Make a low-poly 3D model of Arjun G Round Roller."
},
{
"role": "assistant",
"content": "The mesh below uses OBJ syntax with coordinates quantized to a 64-value grid inside a unit cube.\n\nv 25 24 0\nv 35 24 0\nv 26 33 0\nv 35 33 0\nv 30 19 1\nv 25 21 1\nv 36 21 1\n... |
00000054c36d44a2a483bdbff31d8edf | mesh_to_text | [
{
"role": "user",
"content": "Describe the object defined by this OBJ mesh.\n\nThe mesh below uses OBJ syntax with coordinates quantized to a 64-value grid inside a unit cube.\n\nv 25 24 0\nv 35 24 0\nv 26 33 0\nv 35 33 0\nv 30 19 1\nv 25 21 1\nv 36 21 1\nv 22 23 1\nv 39 23 1\nv 25 24 1\nv 35 24 1\nv 21 28 ... |
00001ec0d78549e1b8c2083a06105c29 | text_to_mesh | [
{
"role": "user",
"content": "Produce a triangle mesh of Aguilar0315 Headpt2 in OBJ format."
},
{
"role": "assistant",
"content": "The mesh below uses OBJ syntax with coordinates quantized to a 64-value grid inside a unit cube.\n\nv 19 29 2\nv 44 29 2\nv 18 30 2\nv 23 30 2\nv 40 30 2\nv 45 30 2\... |
00001ec0d78549e1b8c2083a06105c29 | mesh_to_text | [
{
"role": "user",
"content": "Describe the object defined by this OBJ mesh.\n\nThe mesh below uses OBJ syntax with coordinates quantized to a 64-value grid inside a unit cube.\n\nv 19 29 2\nv 44 29 2\nv 18 30 2\nv 23 30 2\nv 40 30 2\nv 45 30 2\nv 8 31 2\nv 15 31 2\nv 48 31 2\nv 55 31 2\nv 8 33 2\nv 15 33 2\... |
00002bcb84af4a4781174e62619f14e2 | text_to_mesh | [
{
"role": "user",
"content": "Generate a 3D mesh of 27141E."
},
{
"role": "assistant",
"content": "The mesh below uses OBJ syntax with coordinates quantized to a 64-value grid inside a unit cube.\n\nv 59 4 0\nv 5 5 0\nv 58 5 0\nv 6 6 0\nv 57 6 0\nv 56 7 0\nv 7 56 0\nv 6 57 0\nv 57 57 0\nv 5 58 0... |
00002bcb84af4a4781174e62619f14e2 | mesh_to_text | [
{
"role": "user",
"content": "What does this 3D mesh represent?\n\nThe mesh below uses OBJ syntax with coordinates quantized to a 64-value grid inside a unit cube.\n\nv 59 4 0\nv 5 5 0\nv 58 5 0\nv 6 6 0\nv 57 6 0\nv 56 7 0\nv 7 56 0\nv 6 57 0\nv 57 57 0\nv 5 58 0\nv 58 58 0\nv 59 59 0\nv 7 7 23\nv 4 59 23\... |
0000627f531e4fbc8668b79be7471b8b | text_to_mesh | [
{
"role": "user",
"content": "Generate a 3D mesh of Conducto inguinal."
},
{
"role": "assistant",
"content": "The mesh below uses OBJ syntax with coordinates quantized to a 64-value grid inside a unit cube.\n\nv 48 51 16\nv 48 62 20\nv 42 52 21\nv 35 39 22\nv 41 43 22\nv 38 44 22\nv 41 45 22\nv ... |
0000627f531e4fbc8668b79be7471b8b | mesh_to_text | [
{
"role": "user",
"content": "Identify the object represented by the following mesh.\n\nThe mesh below uses OBJ syntax with coordinates quantized to a 64-value grid inside a unit cube.\n\nv 48 51 16\nv 48 62 20\nv 42 52 21\nv 35 39 22\nv 41 43 22\nv 38 44 22\nv 41 45 22\nv 39 46 22\nv 43 49 22\nv 43 51 22\n... |
000074a334c541878360457c672b6c2e | text_to_mesh | [
{
"role": "user",
"content": "Generate a 3D mesh of Worn Flip Flop (fashion-style)."
},
{
"role": "assistant",
"content": "The mesh below uses OBJ syntax with coordinates quantized to a 64-value grid inside a unit cube.\n\nv 56 17 22\nv 36 19 22\nv 42 19 22\nv 43 23 22\nv 40 24 22\nv 47 25 22\nv... |
000074a334c541878360457c672b6c2e | mesh_to_text | [{"role":"user","content":"Describe the object defined by this OBJ mesh.\n\nThe mesh below uses OBJ (...TRUNCATED) |
End of preview.
3D Model Dataset (built from allenai/objaverse)
Filtered, Blender-validated subset of allenai/objaverse. Model files are preserved byte-for-byte from the source; each record carries Blender-extracted geometry, materials, textures, scene structure, and hierarchy.
Layout
data/shard-XXXXXX/models/ original model files ({uid}.glb)
data/shard-XXXXXX/metadata.jsonl one record per model
data/shard-XXXXXX/train.jsonl chat-format LLM fine-tuning pairs
data/shard-XXXXXX/_COMPLETE upload-verified marker (immutable)
dataset_info/processing_stats.json cumulative stats
dataset_info/rejected_models.jsonl rejected uids with reasons
LLM fine-tuning pairs (train.jsonl)
Each line is a chat-format example ready for standard SFT stacks
(HF trl, Axolotl, ...):
{"id": "<uid>", "task": "text_to_mesh" | "mesh_to_text",
"messages": [{"role": "user", "content": "..."},
{"role": "assistant", "content": "..."}]}
text_to_mesh: the prompt asks for an object; the completion is the mesh as plain OBJ text — decimated in Blender, normalized to a unit cube, coordinates quantized to integers, vertices/faces in canonical order.mesh_to_text: the reverse direction (mesh understanding / description).- Pairs are only emitted when the source annotation has a usable name/category;
captions are never invented. The per-model
llm_meshblock in metadata.jsonl records whether a model was serialized and why not if it wasn't.
Stats (as of last completed shard)
{
"total_selected": 712274,
"total_downloaded": 47400,
"total_valid": 38705,
"total_invalid": 1515,
"total_uploaded": 38541,
"completed_shards": 312,
"licenses": {
"CC-BY-4.0": 37524,
"CC-BY-SA-4.0": 812,
"CC0-1.0": 205
},
"formats": {
"glb": 38541
},
"blender_crashes": 4
}
Notes
- Allowed licenses: CC0-1.0, CC-BY-4.0, CC-BY-SA-4.0. Every record stores
source.license(SPDX-style) andsource.license_raw(original slug), plus creator and source URL for attribution. - The CC version (4.0) in license IDs is an assumption — the source slug
(e.g.
by) does not encode a version. - Blender-extracted vertex/face counts intentionally differ from the source's
reported counts (importers count split vertices differently); the original
annotation is preserved unchanged in
original_metadata. - A shard directory with a
_COMPLETEmarker is verified and immutable.
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