InclusiveHuman-10K: Towards Inclusive Human Parsing Beyond the Intact-Limb Assumption

Human body parsing datasets and benchmarks define partlabels for intact anatomies and focus on body parts and clothing. Widelyused datasets implicitly assume intact anatomies and therefore do not in-clude residual-limb or prosthetic classes. This leaves people with limb de-ficiencies excluded from body parsing model training and standard evalu-ation, creating a fairness gap. To the best of our knowledge, InclusiveHuman-10K (IH-10K) is the first human parsing benchmark centered on in-dividuals with limb deficiencies, with explicit labels for residual limbs,prostheses, and mobility assistive devices. Specifically, IH-10K containsmore than 10k images and around 15k person instances from everyday,sports, and clinical scenes, each with pixel-wise body parsing masks. Thisbenchmark can support e-commerce, human-machine interaction, reha-bilitation and assistive technology, human-centric image understanding,and editing. Moreover, we extend conventional body parsing categorieswith explicit classes for residual limbs, prostheses, and mobility assis-tive devices. On our IH-10K, body parsing models can be trained andevaluated on disability-related regions for this population with limb de-ficiencies, instead of treating them as background or noise. To assesswhether disability-related regions are systematically underserved, we re-port group-wise evaluation metrics across disability-related and commonclasses. Our experiments show that supervised models struggle on theseregions due to large appearance variation and broken limb structure.Meanwhile, zero-shot methods perform poorly, suggesting that these con-cepts are absent from previous benchmarks and under-covered in open-vocabulary pretraining. This further highlights the representational andmeasurement gaps and signifies the necessity of our IH-10K benchmark.We hope this work helps build vision systems that serve people with limbdeficiencies more reliably and fairly, increasing the likelihood of mean-ingful social benefit.