HandVQA: Diagnosing and Improving Fine-Grained Spatial Reasoning about Hands in Vision-Language Models

CVPR 2026

MD Khalequzzaman Chowdhury Sayem^1*, Mubarrat Tajoar Chowdhury^1*, Yihalem Yimolal Tiruneh¹, Muneeb A. Khan¹, Muhammad Salman Ali¹, Binod Bhattarai^2,3,4†, Seungryul Baek^1†

¹UNIST, ²University of Aberdeen, ³University College London, ⁴Fogsphere (Redev.AI Ltd)

^*Equal contribution. ^†These authors jointly supervised this work.

HandVQA is a large-scale diagnostic benchmark designed to evaluate fine-grained spatial reasoning in vision-language models (VLMs), focusing on articulated hand pose understanding.

It contains over 1.6 million multiple-choice questions (MCQs) derived from 3D hand annotations, probing joint-level relationships such as angles, distances, and relative positions.

Dataset Description

Motivation

Despite strong performance on general VQA tasks, VLMs struggle with fine-grained spatial reasoning, especially for articulated structures like human hands.

HandVQA is designed to diagnose these limitations by evaluating:

• Joint angle understanding
• Inter-joint distances
• Relative spatial positions (X, Y, Z)

Data Sources

The dataset is built from:

• FreiHAND
• InterHand2.6M
• FPHA

using their 3D hand joint annotations.

Task Format

Each sample consists of:

• An image of a hand
• A multiple-choice question (MCQ)
• 4 candidate answers
• 1 correct answer

Subtasks

HandVQA includes 5 categories:

1. Angle
2. Distance
3. Relative Position (X-axis)
4. Relative Position (Y-axis)
5. Relative Position (Z-axis)

Each question probes a specific geometric relation between hand joints.

Example

Question: From the options below, choose the correct description.

Options:

A. The middle finger is bent completely inward at the distal interphalangeal joint.

B. The middle finger is bent inward at the distal interphalangeal joint.

C. The middle finger is bent slightly inward at the distal interphalangeal joint.

D. The middle finger is straight at the distal interphalangeal joint.

Answer: D

Dataset Statistics

• Total questions: ~1.6M+
• Number of datasets used: 3
• Categories: 5

Data Generation Pipeline

HandVQA is generated using a deterministic pipeline:

1. Pose Descriptor Extraction

   • Compute angles, distances, and relative positions from 3D joints

2. Discretization

   • Convert continuous values into categories (e.g., bent, straight)

3. Sentence Generation

   • Fill structured templates

4. MCQ Formation

   • Generate correct + distractor answers

Intended Uses

• Benchmarking spatial reasoning in VLMs
• Training spatially-aware multimodal models
• Evaluating hallucination in pose understanding
• Studying geometry-grounded reasoning

Evaluation Metrics

• Accuracy
• Mean Absolute Error (MAE) for ordinal tasks (angle, distance)

HandVQA evaluates whether models truly understand spatial geometry rather than relying on language priors.

Citation

@inproceedings{sayem2026handvqa,
  title     = {HandVQA: Diagnosing and Improving Fine-Grained Spatial Reasoning about Hands in Vision-Language Models},
  author    = {Sayem, MD Khalequzzaman Chowdhury and Chowdhury, Mubarrat Tajoar and Tiruneh, Yihalem Yimolal and Khan, Muneeb A. and Ali, Muhammad Salman and Bhattarai, Binod and Baek, Seungryul},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2026},
  note      = {arXiv:2603.26362}
}