mzhobro/taco_dataset_resized

TACO Resized (512x376) — Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding

This is the resized version of the TACO dataset, with all allocentric videos and segmentation masks downscaled to a uniform 512x376 resolution (from native 4096x3000 / 2048x1500). Camera intrinsics are rescaled accordingly.

Why use this version?

The original TACO allocentric videos are 4096x3000, making training impractical without on-the-fly resizing. This version pre-processes everything to 512x376, resulting in ~25x faster data loading and 4.4x less disk space.

Loading Performance Comparison

Config: 4 context + 3 target views, 4 past + 8 future frames (52 frame decodes/sample). Cold loading, no cache.

	Original (4096x3000)	Resized (512x376)	Speedup
Single sample (no seg)	3.91 s	158 ms	25x
Single sample (with seg)	4.23 s	169 ms	25x
Throughput (4 workers)	0.52 samp/s	12.2 samp/s	23x
Disk size	2.2 TB	495 GB	4.4x smaller
Allocentric videos	809 GB	4.3 GB	188x smaller
Segmentation masks	632 GB	145 GB	4.4x smaller

Full profiling details: tools/taco_analysis/profile_comparison.md

What changed

• Allocentric RGB videos: resized from 4096x3000 / 2048x1500 to 512x376, re-encoded as H.264 MP4
• 2D segmentation masks: resized from 750x1024 to 375x512 using nearest-neighbor interpolation
• Calibration intrinsics: K matrix scaled to match new resolution, imgSize updated to [512, 376]
• Everything else unchanged: egocentric videos, depth, hand poses, object poses, object models, MANO

Dataset Contents

• 2120 motion sequences (from Version 1's 2317, filtered to marker-removed)
• 12 allocentric cameras per sequence at 512x376
• 2D segmentation masks at 375x512
• Egocentric RGB-D videos (original resolution)
• Hand-object pose annotations + pre-computed 3D hand joints
• 206 high-resolution object models
• Camera parameters (intrinsics rescaled)

Archive Contents

Archive	Size	Contents
Marker_Removed_Allocentric_RGB_Videos.zip	4.3 GB	12 camera MP4s per sequence (512x376)
2D_Segmentation.zip	145 GB	Per-camera segmentation masks (375x512 npy)
Hand_Poses.zip	25.9 GB	MANO params (pkl) + pre-computed 3D joints (npy)
Hand_Poses_3D.zip	160 MB	3D joints only — hand_joints.npy per sequence (T, 2, 21, 3) float32
Object_Poses.zip	64 MB	Object 6DoF transforms (npy)
Egocentric_RGB_Videos.zip	641 MB	Egocentric RGB videos
Egocentric_Depth_Videos.zip.*	959 MB	Egocentric depth videos (split archive)
object_models_released.zip	~1.2 GB	206 high-res object meshes
mano_v1_2.zip	small	MANO hand model files

Allocentric_Camera_Parameters/ and taco_info.csv are stored directly (not zipped).

Tip: If you only need 3D hand joints (not raw MANO parameters), download Hand_Poses_3D.zip (160 MB) instead of Hand_Poses.zip (26 GB).

Downloading

# Full download
huggingface-cli download mzhobro/taco_dataset_resized \
    --repo-type dataset \
    --local-dir taco_dataset_resized

cd taco_dataset_resized
# Reassemble split archives and extract
./reassemble.sh
for z in *.zip; do unzip -qn "$z"; done

# Minimal download (allocentric videos + cameras + 3D hand joints only)
huggingface-cli download mzhobro/taco_dataset_resized \
    --repo-type dataset \
    --include "*.csv" "*.sh" "*.md" \
              "Marker_Removed_Allocentric_RGB_Videos.zip" \
              "Allocentric_Camera_Parameters/**" \
              "Hand_Poses_3D.zip" \
    --local-dir taco_dataset_resized

Hand Poses 3D Format

Hand_Poses_3D/{action}/{sequence_id}/hand_joints.npy — shape (T, 2, 21, 3):

• T: number of frames
• Dim 1: 0=left hand, 1=right hand
• 21 joints: wrist, index(MCP,PIP,DIP,tip), middle(...), ring(...), pinky(...), thumb(CMC,MCP,IP,tip)
• 3: xyz world coordinates in meters

# Loading in the dataset loader
ds = TACODataset(..., load_hand_joints=True)
sample = ds[0]
sample["hand_joints"]  # (T, 2, 21, 3) float32

Related

• Original full-resolution dataset: mzhobro/taco_dataset

Tools

The tools/ directory contains:

• taco_dataset_loader.py — PyTorch Dataset class for loading TACO data
• view_sampler.py — Camera view sampling strategies
• generate_taco_csv.py — Generate taco_info.csv metadata
• precompute_hand_joints.py — Pre-compute 3D hand joints from MANO parameters
• taco_analysis/ — Analysis and visualization scripts (dataset stats, camera extrinsics, epipolar lines, mesh overlays, profiling)
• resizing_pipeline/ — Scripts used to produce this resized dataset from the original

cd tools/taco_analysis

# Dataset summary statistics
python analyze_taco.py

# Camera extrinsics analysis
python analyze_extrinsics.py

# Loading performance profile
python profile_dataset.py --root ../../ --output profile.md

# Visualizations
python visualize_taco_3d_scene.py
python visualize_taco_cameras_topdown.py
python visualize_taco_epipolar.py
python render_mesh_overlay.py

Each script accepts --help for full options.