Omkar2118/datasets huggingface dataset:
ValiMath

ValiMath is a high-quality benchmark consisting of 2,147 carefully curated mathematical questions designed to evaluate an LLM's ability to verify the correctness of math questions based on multiple logic-based and structural criteria.

Introducing paper: Let's Verify Math Questions Step by Step
TSBench

For slide editing, this benchmark dataset provide the pair of user instruction and corresponding slides (pptx).

Introducing paper: Talk to Your Slides: Language-Driven Agents for Efficient Slide Editing
ObMan
Full name: synthetic Object Manipulation

The ObMan dataset is a a large-scale synthetic image dataset of hands grasping objects. Body poses are sampled from MoCap data, and hand poses are generated by the automatic robotic grasping software GraspIt. The realistic body model SMPL+H is rendered grasping ShapeNet object models under a large variation in pose, background, texture, and lighting. 150K images are generated, along with ground truth 3D hand and object meshes, 2D/3D hand keypoints, object and hand segmentations, and depth maps.
HO3D

A dataset for pose estimation of hand when interacting with object and severe occlusions.
H2O3D

A dataset for pose estimation of hand when interacting with object and severe occlusions.
Decaf
Full name: deformation capture of faces interacting with hands

We introduce the first monocular motion capture method from a video that regresses 3D hand and face motions along with deformations arising from their interactions. We model hands as articulated objects inducing non-rigid face deformations during an active interaction. Our method relies on a new hand-face motion and interaction capture dataset with realistic face deformations acquired with a markerless multi-view camera system. As a pivotal step in its creation, we process the reconstructed raw 3D shapes with position-based dynamics and an approach for non-uniform stiffness estimation of the head tissues, which results in plausible annotations of the surface deformations, hand-face contact regions and head-hand positions. At the core of our neural approach are a variational auto-encoder supplying the hand-face depth prior and modules that guide the 3D tracking by estimating the contacts and the deformations. Our final 3D hand and face reconstructions are realistic and more plausible compared to several baselines applicable in our setting, both quantitatively and qualitatively.
WebShop
Full name: Shunyu Yao

https://proceedings.neurips.cc/paper_files/paper/2022/hash/82ad13ec01f9fe44c01cb91814fd7b8c-Abstract-Conference.html
X-WebAgentBench

https://arxiv.org/abs/2505.15372

Introducing paper: X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System