MV-Fashion/MV-Fashion

🗺️ OverMaps-1K Dataset OverMaps-1K represents the convergence of classical 3D reconstruction rigor with the massive data requirements of modern Generative AI. Designed to bridge the gap between scale and quality, this dataset moves beyond the limitations of uncontrolled web videos or synthetic renders. It provides a foundation for training spatial reasoning in foundation models, advancing neural rendering, and developing robotic perception systems that can… See the full description on the dataset page: https://huggingface.co/datasets/OverTheReality/OverMaps_1k.

QueST: PartNet-Mobility SAPIEN Simulation Dataset This dataset accompanies the paper: QueST: Persistent Queries as Semantic Monitors for Drift Suppression in Long-Horizon TrackingMayank Anand, Mohammad Saqlain, Kyan Mahajan, Priya Shukla, G.C Nandi, Andrew MelnikCAO Workshop at ICLR 2026 What Is This Dataset? Synchronized RGB-D simulation sequences rendered in SAPIEN from PartNet-Mobility articulated objects, designed to stress-test long-horizon point… See the full description on the dataset page: https://huggingface.co/datasets/AnandMayank/QueST-PartNetMobility-SAPIEN.

Garments2Look [CVPR 2026] Paper: Garments2Look: A Multi-Reference Dataset for High-Fidelity Outfit-Level Virtual Try-On with Clothing and Accessories Authors: Junyao Hu, Zhongwei Cheng, Waikeung Wong, Xingxing Zou Paper: arXiv, CVPR, 中译版 Project Page Poster Dataset LoRA weights and inference examples Code and dataset preparation Comparison Results on Test Set Release history 2026-10-11 — Qwen-Image-2.1 LoRA release. Final inpainting and editing adapters… See the full description on the dataset page: https://huggingface.co/datasets/ArtmeScienceLab/Garments2Look.

FIT: A Large-Scale Dataset for Fit-Aware Virtual Try-On The official preview dataset from the paper "FIT: A Large-Scale Dataset for Fit-Aware Virtual Try-On". This dataset supports garment-centric virtual try-on and try-off research, containing 100,000 training and 5,000 evaluation triplets. Each sample pairs a person image with a layflat garment image and body/garment measurements. Dataset Structure Each split (train / eval) contains four aligned modalities — all… See the full description on the dataset page: https://huggingface.co/datasets/Yuanhao-Harry-Wang/fitvto-100k.

Virtual TryOn for commercial purpose. virtual try on research papers, virtual try-on projects, try on code, virtual try-on dataset, virtual try on workshops related to virtual try-on. .

⚠️ Important: If you have already submitted an access request but have not completed the required DocuSign agreement, your request will remain pending. Please complete signing and we will grant access once verified. Interactive Intelligence from Human Xperience Xperience-10M Dataset Summary Xperience-10M is a large-scale egocentric multimodal dataset of human experience for embodied AI, robotics, world models, and spatial… See the full description on the dataset page: https://huggingface.co/datasets/ropedia-ai/xperience-10m.

Stera-10M Visualizer: https://platform.fpvlabs.ai/dataset/stera-10m/viz Dataset Summary Stera-10M is an open egocentric multimodal dataset for embodied AI, robotics, world models, and spatial intelligence, captured end-to-end on commodity iPhone Pro hardware through the open Stera platform. It contains 200 hours of synchronized first-person recordings across 500+ sessions from 20 contributors in 20+ unique environments, with 10 million RGB frames, LiDAR depth… See the full description on the dataset page: https://huggingface.co/datasets/fpvlabs/stera-10m.

ACE-Data-0 Human-Centric Ambient Capture as Embodied Data Engine S-Lab, Nanyang Technological University, Singapore  ·  ACE Robotics ACE turns real home environments into spatially calibrated, temporally synchronized recording studios for embodied AI. ▶ Demo video  ·  Full story, figures, and interactive examples on the blog What this is Learning to act in the physical… See the full description on the dataset page: https://huggingface.co/datasets/ACERobotics/ACE-Data-0.

Stereo Dataset Dataset Summary StereoDataset is a synthetic multiview stereo dataset rendered in Unreal Engine. Each scene combines a map, a character mesh, an animation clip, a validated spawn location, and a camera trajectory. The released scene folders contain synchronized per-camera RGB videos (cam_XX_rgb.mp4), per-camera depth videos (cam_XX_depth.mkv), and scene-level metadata including _scene_complete.json, trajectory.json, and baseline.json. The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/stereo-dataset/stereo-dataset.