hold logo

hold

Enrichment pending
zc-alexfan/hold

[CVPR 2024✨Highlight] Official repository for HOLD, the first method that jointly reconstructs articulated hands and objects from monocular videos without assuming a pre-scanned object template and 3D

GraphCanon updated today · GitHub synced today

486
Stars
15
Forks
9
Open issues
11
Watchers
4mo
Last push
Python MITCreated Nov 30, 2023

Trust & integrity

Full report
Maintenance
Slowing (122d since push)
As of today · Source: github_public_v1
Provenance
Not a fork · Personal account
As of today · Source: github_public_v1
Security (OSV)
9 low (9 low)
As of today · Source: osv@v1

Public GitHub metadata and optional OSV dependency scans. Signals, not a guarantee. Trust methodology.

Overview

[CVPR 2024✨Highlight] Official repository for HOLD, the first method that jointly reconstructs articulated hands and objects from monocular videos without assuming a pre-scanned object template and 3D hand-object training data.

Capability facts

Languages
python

Source: github.language · Jul 11, 2026

Categories

Tags

README

Getting started

Get a copy of the code:

git clone https://github.com/zc-alexfan/hold.git
cd hold; git submodule update --init --recursive
  1. Setup environments

    • Follow the instructions here: docs/setup.md.
    • You may skip external dependencies for now.
  2. Train on a preprocessed sequence

    • Start with one of our preprocessed in-the-wild sequences, such as hold_bottle1_itw.
    • Familiarize yourself with the usage guidelines in docs/usage.md for this preprocessed sequence.
    • This will enable you to train, render HOLD, and experiment with our interactive viewer.
    • At this stage, you can also explore the HOLD code in the ./code directory.
  3. Set up external dependencies and process custom videos

    • After understanding the initial tools, set up the "external dependencies" as outlined in docs/setup.md.
    • Preprocess the images from the hold_bottle1_itw sequence by following the instructions in docs/custom.md.
    • Train on this sequence to learn how to build a custom dataset.
    • You can capture your own custom video and reconstruct it in 3D at this point.
    • Most preprocessing artifact files are documented in docs/data_doc.md, which you can use as a reference.
  4. Two-hand setting: Bimanual category-agnostic reconstruction

    • At this point, you can preprocess and train on a custom single-hand sequence.
    • Now you can take on the bimanual category-agnostic reconstruction challenge!
    • Following the instruction in docs/arctic.md to reconstruct two-hand manipulation of ARCTIC sequences.