InclusiveVidPose: Bridging the Pose Estimation Gap for Individuals with Limb Deficiencies in Video-Based Motion
Authors: Heming Du
Jiaying Ying
Sen Wang
Xue Li
Kaihao Zhang
Xin Yu
Venue: ICLR 2026 Poster (2026)
Keywords: Disabled person
Individuals with limb deficiencies
dataset
human pose estimation
Primary area: datasets and benchmarks
TL;DR: InclusiveVidPose is a large video HPE dataset for people with limb differences with residual-limb keypoints and rich labels, plus the LiCC metric and a benchmark, showing current methods struggle and motivating fairer, more accurate pose estimation.

Abstract: Approximately 445.2 million individuals worldwide are living with traumatic amputations, and an estimated 31.64 million children aged 0–14 have congenital limb differences, yet they remain largely underrepresented in human pose estimation (HPE) research. Accurate HPE could significantly benefit this population in applications, such as rehabilitation monitoring and health assessment. However, the existing HPE datasets and methods assume that humans possess a full complement of upper and lower extremities and fail to model missing or altered limbs. As a result, people with limb deficiencies remain largely underrepresented, and current models cannot generalize to their unique anatomies or predict absent joints. To bridge this gap, we introduce InclusiveVidPose Dataset, the first video-based large-scale HPE dataset specific for individuals with limb deficiencies. We collect 313 videos, totaling 327k frames, and covering nearly 400 individuals with amputations, congenital limb differences, and prosthetic limbs. We adopt 8 extra keypoints at each residual limb end to capture individual anatomical variations. Under the guidance of an internationally accredited para-athletics classifier, we annotate each frame with pose keypoints, segmentation masks, bounding boxes, tracking IDs, and per-limb prosthesis status. Experiments on InclusiveVidPose highlight the limitations of the existing HPE models for individuals with limb deficiencies. We introduce a new evaluation metric, Limb-specific Confidence Consistency (LiCC), which assesses the consistency of pose estimations between residual and intact limb keypoints. We also provide a rigorous benchmark for evaluating inclusive and robust pose estimation algorithms, demonstrating that our dataset poses significant challenges. We hope InclusiveVidPose spur research toward methods that fairly and accurately serve all body types. The project website is available at: [InclusiveVidPose](https://anonymous-accept.github.io/inclusivevidpose/).

Published as a conference paper at ICLR 2026
INCLUSIVEVIDPOSE: BRIDGING THE POSE ESTIMA- TION GAP FOR INDIVIDUALS WITH LIMB DEFICIENCIES IN VIDEOS
Heming Du; Jiaying Ying, Sen Wang & Xue Li School of Electrical Engineering and Computer Science The University of Queensland
Kaihao Zhang
College of Engineering and Computer Science The Australian National University
Xin Yu?
Australian Institute for Machine Learning The University of Adelaide
Residual Limb End
Left Upper-L -in--Liitt Limb &
Deficiency
Right Upper-Limm Limb Deftoency
Figure 1: Demonstration of IndlusiveVidPose Dataset. The top row depicts a subject with left upper and lower limb deficiency across frames, and the bottom row depicts a subject with right upper limb deficiency. Unlike existing pose estimation datasets, we focus on human pose estimation for individuals with limb deficiencics and add custom residual limb end kcypoints highlighted in purple to capture anatomical variations. All the displayed keypoints are manually annotated.
ABSTRACT
Approximately 445.2 million individuals worldwide are living with traumatic am- putations, and an estimated 31.64 million children aged 0-14 have congenital limb differences, yet they remain largely underrepresented in human pose estimation (HPE) research. Accurate HPE could significantly benefit this population in ap- plications, such rehabilitation monitoring and health assessment. However, the existing HPE datasets and methods assume that humans possess full complement of upper and lower extremities and fail model missing altered limbs. As result, people with limb deficiencies remain largely underrepresented, and current models cannot generalize to their unique anatomies predict absent joints. To bridge this gap, we introduce Inclusive VidPose Dataset, the first video-based large-scale HPE dataset specific for individuals with limb deficiencies. We col- lect 313 videos, totaling 327k frames, and covering nearly 400 individuals with amputations, congenital limb differences. and prosthetic limbs. We adopt extra keypoints at each residual limb end to capture individual anatomical variations. Under the guidance of an internationally accredited para-athletics classifier, we annotate each frame with pose keypoints, segmentation masks, bounding boxes, tracking IDs, and per-limb prosthesis status. Experiments on Inclusive InclusiveVidPose highlight the limitations of the existing HPE models for individuals with limb
Equaa contribution. Corresponding Author

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Published as a conference paper at ICLR 2026
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Figure 2: Examples of keypoint predictions by the ViTPose base model trained the COCO dataset. Left: prostheses are erroneously predicted as natural ankles, leading to false righl-ankle detections. Middle: the model fails to localize the residual limb end and places the left-wrist keypoint on the torso. Right: pronounced asymmetry in thigh length results in the right-knee keypoint being placed at anatomically implausible midpoint between hip and ankle. These cases show limited generalization to limb differences.
deficiencies. We introduce new evaluation metric, Limb-specific Confidence Consistency (LiCC), which assesses the consistency of pose estimations between residual and intact limb keypoints. We also provide rigorous benchmark for evaluating inclusive and robust pose estimation algorithms, demonstrating that our dataset poses significant challenges. We hope ViclussveVidPPoe spur research toward methods that fairly and accurately serve all body types. The project website is available at: O InclusiveVidPose
INTRODUCTION
According to the Global Burden of Disease (GBD) 2021 report (Institute for Health Metrics and Evaluation (IHME) 2024), approximately 445.2 million individuals worldwide are living with traumatic amputations, and an estimated 31.64 million children aged 0-14 have congenital limb differences. These people could benefit greatly from accurate human pose estimation (HPE) in applications such as rehabilitation monitoring and health assessment. Regrettably, existing HPE systems are trained on the existing datasets and are not designed for atypical anatomies or prosthetic occlusions, leaving this population unsupported.
Widely used benchmarks like MS COCO (Lin et al. 2014b) and MPII Human Pose (Andriluka al. 2014) include only able-bodied people with complete sets of keypoints. These datasets and the methods built on them assume that every keypoint a presented individual exists, making no provision for missing altered limbs. As demonstrated in Figure 2 the MS COCO trained ViTPose model produces significant errors when applied to images of individuals with limb deficiencies. On the basis of these observations, we find that people with limb deficiencies excluded from current research, and models trained on the existing datasets fail to generalize to their anatomies.
To address this gap, we introduce veclusiveVidPosse the first video-based HPE dataset focused on individuals with limb deficiencies. We collect 313 videos, totaling 327k frames, from 398 participants who have amputations, congenital limb differences, and prosthetic limbs. Developing such a dataset presents unique challenges. As illustrated in Figures [] impairments vary widely across individuals, including differences in the appearance and length of residual limbs, their anatomical location, and the effects of different types of prostheses. In the existing keypoint schema, there is no predefined sequence that accommodates these anatomical variations. To more accurately represent these cases, we adopt additional keypoints at the ends of residual limbs.
Unlike single-image datasets, Inclusive leverages video context to disambiguate occluded limbs from truly absent ones, ensuring precise residual-limb end annotations across frames. We ensure the annotation quality through guidance from an internationally accredited para-athletics classifier and verification by multiple trained annotators. For each frame, we provide standard and residual- sssduualllimb keypoints, segmentation masks, subject tracking IDs, bounding boxes, and the prosthesis status of each residual limb.
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Table I: Comparison of existing datascts for human pose estimation. InclusiveVidPose offers unique and richer annotations than previous datasets. is also the first pose estimation dataset to focus on individuals with limb deficiencies and to include keypoints at the ends of residual limbs.
Individuals
Residual
#image/ Frames
Segmentation Mask
Bounding
Tracking
Prosthesis Info
Dataset
#Pose
Box
with Limb
Limb End
Deficienciss
Annotations
MPIT Homan Pose Andriluka et
2014)
25k
40k
Lic (2044)
200k
250k
CrowdPose Lr (219
20k
80k
OCTumm Zhing 2019
I leee Ex|Prse
2.5k
14k
Hluman-Arpu
2412)) F4126)
50k
20seTTack20111 Andrluka
2018
46k
144k
Pos:Trok21 Docring et al L
66k
177k
InclusiveVidPose (Ours)
327k
309k
VidPsoe not only demonstrates the limitations of standard HPE models when applied to people with limb deficiencies but also provides a rigorous benchmark for developing more inclusive HPE algorithms. We evaluate six methods on predictions for missing or prosthetic limbs. Since existing HPE metrics (i.e. OKS (Lin et al. 2014a)) do not account for a model's ability to recognize missing limbs, we propose a new metric, namely Limb-specific Confidence Consistency (LiCC), to quantify this effect. LiCC measures how well the predictions adhere to anatomical exclusion rules. Our results highlight the need for techniques that yield well-calibrated pose estimations and generalize across all body types.
and find that they produce unreliable
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RELATED WORK
2.1 HUMAN POSE DATASETS
High-quality pose datasets are foundational to progress in human pose estimation (HPE), with nu- merous recent relcases offering varied formats and focusing scale, activity diversity, and visual complexity. Among 2D image-based datasets, MSCOCO (Lin et al.]
2014b) is widely used for
its large-scale annotations, while MPII Human Pose (Andriluka et al. 2014 emphasizes action
diversity. CrowdPose (Li et al.) 2019) and OCHuman (Zhang et al.) 2019) focus crowded scenes. Domain-specific datasets include HumanArt (Ju et al.
2023) for natural-artiffcial scene bridging,
LSP (Johnson & Everingham (01IT) for sports, and Pose (Lee et al. 2023) for extreme lighting conditions. Video-based datasets like PoseTrack2018 (Andriluka al. 2018) and PoseTrack21 (Do ering et al., 2022) provide per-frame body keypoint annotations. Additionally, 3D datasets such as Human3. 6M (lonescu al., 2014), GoPose (Ren et al. 2022), and FreeMan (Wang et al.] 2024) support depth-aware modeling for 3D pose estimation. While these datasets address various chal- lenges in HPE, they consistently operate under the assumption of anatomically intact individuals, overlooking individuals with limb deficiencies. This gap raises concerns around fairness, inclusivity, and limits the generalizability of existing HPE models in real-world diverse populations.
Compared to the datasets in Table InclusiveVidPose uniquely centers individuals with limb deficiencies, providing a large-scale, video-based resource. The participants cover a wide range of anatomical variations and prosthetic types, including differences in the shape of residual limbs and the appearance of prostheses. InclusiveVidPose VidPose covers a wide range of limb difference types, including acquired amputations and congenital conditions. Furthermore, we leverage temporal continuity to improve residual limb keypoint annotation, addressing ambiguity common in image-based datasets. We provide rich annotations including 2D poses, segmentation masks, bounding boxes, and tracking IDs, supporting tasks such as pose estimation, pose tracking, and motion analysis.
2.2 HUMAN POSE ESTIMATION MODELS
With increasing demand for real-world applications, human pose estimation has rapidly advanced, with diverse models emerging to optimize accuracy, efficiency, and robustness. Accuracy-oriented models include ViTPose (Xu et al. 2022), which leverages Vision Transformers, AlphaPose (Fang
et al. 2022), known for robust multi-tat task performance, OpenPose (Cao et al.) 2019), pioneering
bottom-up approach, DWPose(Yann) et al.) 2023) which employs a two-stage distillation strategy with
depth prediction. Lightweigh ght models, such YOLOPose (Maji al. 2022) and RTMPose (He
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17: Above Left Flow Residual Limb End
Aboue Right Flbow Lesb End
19. Below Left lbow Residual
20: Below foow Residual Limb
Peekoual Limb Procthesle
17
19
20
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Figure 3: Demonstration of Our Keypoint Schema. Our extended pose definition is built on the MS COCO keypoint schema by adding eight residual limb end keypoints (17 through 24). The left panel shows the full skeleton: original COCO keypoints are connected by solid lines, and the eight new residual limb end keypoints are marked as purple circles. The right panel presents, for each residual limb end keypoint, cropped view of subject with the corresponding amputation and matching view of the same subject wearing a prosthetic device.
et al. 2024), are designed for real-time deployment, prioritizing efficiency and fast inference. In addition, DEKR (Geng et al. 202T) introduces decoupled keypoint regression to improve efficiency in multi-person pose estimation. SAPIENS (Khirodkar et al.] 2024) have demonstrated the benefits of synthetic data integration, improving generalization across diverse real-world contexts.
2.3 HUMAN POSE ESTIMATION FOR HEALTHCARE
Millions globally live with limb deficiencies, facing physical and psychological challenges such as
limited mobility, reduced independence, and mental health impacts (Martin 2013 Day et al.! 2019).
Advances in assistive technologies including prosthetics (Farina et al.] 2023 Andersen al. 2005
Olsen et al.] (022). motion tracking and analysis tools (Zhou & Hu 2008 Parks et al.] 2019) have
improved quality of life. Meanwhile, pose estimation has gained traction in healthcare for applications
like rehabilitation and mobility assessment, such as (Alruwaili et al. 2023 2024 Zhang et al.) 2024).
Efforts like WheelPose (Huang al., (024), which uses synthetic data, and WheelPoser (Li et al.) 2024), which leverages IMU signals. have aimed to support pose estimation for marginalized groups ProGait (Yin al., 2025) is a 2025 published dataset focus on transfemoral prosthesis users and explicitly focused on lower-limb gait. Compared with these datasets, ours covers more limb deficiency types beyond transfemoral gait, includes whole-body and residual limb keypoints with personalized schema, and adds frame-level prosthesis status, segmentation, boxes, and tracking on videos.
INCLUSIVEVIDDOSE: FIRST VIDEO POSE DATASET FOR LIMB
DEFICIENCIES
3.1 EMERGING CHALLENGES OF HPE FOR INDIVIDUALS WITH LIMB DEFICIENCIES
Human pose estimation for individuals with limb deficiencies introduces fundamental challenges that are not addressed by existing models and datasets. Existing keypoint- based systems rely on the presence of anatomical keypoints (e.g., wrists, ankles) that may be absent, altered, or anatomically nonstandard in this population, often resulting invalid or anatomically implausible pose predictions. Prosthetic limbs further complicate estimation due to their wide variability in geometry. material, articulation, and movement patterns, which are unseen in prior training data. As illustrated in Figure 2 state-of-the siite-of-the--- arrt models frequently fail when encountering missing joints or prosthetic replacements.
In addition, annotation schemes designed for able-bodied subjects do not generalize to limb deficien- cies. Missing joints must be modeled explicitly, and residual limbs require alternative keypoints. A single fixed skeleton cannot capture this diversity, motivating flexible per- iervindividual schemas and a dedicated video-based dataset.
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Figure 4: Overview of frame-by-trame annotations. Consecutive video frames are shown on the top. For each individual with limb deficiency on every frame, we provide pixel-level segmentation mask, a bounding box, a set of body keypoints, prosthesis status label, and a tracking ID,
3.2 DATA COLLECTION
Why do we build our dataset from videas instead of curated images? In a single image, an occluded limb can appear identical to missing one, creating ambiguity in annotation. Video sequences provide temporal continuity and changing viewpoints. By observing motion or shifts in perspective, annotators can distinguish a limb that is simply hidden from a limb that is truly absent. This capability leads precise labeling of residual-limb end keypoints and reduces errors in pose estimation for individuals with limb deficiencies.
Data sourcing. We collect raw footage from archival materials from the International Paralympic Committee (IPC) and Youeube videos. For IPC materials, we have gathered the permission to store and distribute the content for academic research. Our data collected from YouTube meet the standard for the fair use (U.S.) and fair dealing for research and study (Australia). Furthermore, the project was approved by the institutional Human Research Ethies Committee in September 2024, with approval valid until September 2029. All frames are annotated at the highest available resolution. We preserve associated metadata for traceability, including upload date, user handle, and caption. We plan ongoing maintenance, including periodic refreshes and corrections to annotations.
Data filtering and curation. We manually review all collected data and extract segments that center on individuals with limb deficiencies. We remove clips with severe motion blur or heavy occlusion, such as splashing water, whenever annotators could not agree on keypoint placement. This process yields 313 high-quality videos that span diverse actions, backgrounds and lighting conditions. These curated video segments form the core of our Inclusive dataset.
3.3 HUMAN BODY KEYPOINT SCHEMA FOR INDIVIDUALS WITH LIMB DEFICIECCIES
Necessity of specialized human body keypoint schema. Current pose estimation task uses fixed keypoint sets that fail to capture anatomical variation in individuals with limb deficiencies. Models trained on existing datasets often incorrectly predict complete limbs even when these structures are absent or replaced by prostheses. This mismatch yields invalid outputs and reduces reliability. The core issue is the lack of schema that adapts to residual anatomy. Without keypoints residual-lime -imb endpoints, methods cannot represent true motion. In light of this, adopting specialized set of keypoints would address this gap by aligning keypoint definitions with individual anatomy. This alignment is essential to deliver accurate, relevant, and robust pose estimates for individuals with limb deficiencies.
Design of an extended keypoint schema. As seen in Figure 3 we adopt keypoint schema that builds on the COCO format's 17 keypoints by adding 8 residual-limb points. These eight keypoints lie at the anatomical endpoints of residual limbs (left/right above and below the elbow, left/right above and below the knee) and explicitly exclude any prosthetic or assistive device. In this way, the schema focuses human anatomy rather than device geometry: residual-limb endpoints
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Figure 5: Overview of our VnclusiveVidPool dataset. Sample frames show individuals with a range of limb-differene anatomies, including upper-body impairments, lower-body impairments, single residual limbs, and multiple residual limbs. Mcanwhile, our dataset covers indoor and outdoor environments and wide variety of activities such as rehabilitation training, sports competition, and everyday interactions. All residual limb points and corresponding skeletons are highlighted in purple.
annotated as stable anatomical landmarks. while prosthesis shape and contact with the environment are represented through pixel-level segmentation masks and per-limb prosthesis status. Standard pose- estimation models lack mechanism to represent missing or truncated anatomy, they often try to localize non-existent joints or collapse output when a limb is absent. By incorporating endpoints that correspond the actual residual anatomy, our 25-keypoint protocol gives models clear, semantically meaningful targets that distinguish between intact and residual structures.
3.4 DATA ANNOTATION PROCEDURE
We annotate every individual with five label types: pixel-level segmentation masks, bounding boxes, a persistent tracking ID, limb-deficient body keypoints, and prosthesis limbs, illustrated in Figure
Annotation team training, We recruit 12 annotators through a professional data labeling service. All had prior experience with human body keypoint annotation. Before beginning work, each annotator completed a focused training session led by an internationally accredited para-athletics classifier. This preparation ensured that every team member understood the anatomical variation found in individuals with limb deficiencies and the requirements of our extended keypoint schema.
Bounding box, segmentation mask and tracking ID. We use the open-source platform X- AnyLabeling (Wang] 2023) together with Segment Anything 2 promptable segmentation to generate initial masks. For each individual, annotators first merge all segmentation masks sharing the same tracking ID in given frame and compute the tightest enclosing bounding box around that merged mask. They then refine a pixel-accurate segmentation mask and confirm the persistent tracking ID. SAM2's ability to generalize zero-shot to unseen residual-limb shapes cuts mask-drawing time by over 50%. To maintain high quality, we enforce an 80% accuracy threshold: after one annotator finishes batch, second annotator cross- cross-checks it. Any batch with more than twenty percent of masks showing clear errors is returned for revision.
Human body keypoint annotation process. To capture each subject's unique anatomy, two trained annotators and one accredited para-athletics classifier review all 398 individuals' disability profiles and agree personalized 25-keypoint schema. Each schema is represented by 25-element

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Published as a conference paper at ICLR 2026
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Figure 6: Overview Participant Distribution by Deficiency Type, Gender, and Prosthesis. (a) Distribution of limb-deficiency types, with single-site deficiencies and multi-site cases. (b) Gender Distribution. (c) Distribution of individuals with and without prosthetic limbs.
presence mask that indicates which the 25 keypoints apply to that individual. Annotators then label every frame according to that schema, marking each keypoint's image coordinates. We leverage temporal cues enforce cross- frame consistency and smooth annotations over frames. In addition, for every frame, we record prosthesis status label that specifies which limb or limbs are fitted with prosthesis. We follow the COCO visibility convention: any keypoint that is within the frame but occluded receives visibility flag of 1. We require 80% point-level agreement rate between annotators on 5% of sampled data, where "agreement" means no visually apparent errors in keypoint placement. If more than 20% of sampled points in batch fall below this threshold, the original annotator must correct the entire batch. This structured, expert-guided workflow ensures unambiguous placement of residual-limb keypoints and delivers highly reliable pose data.
3.5
DATASET STATISTICS
Our dataset comprises 313 video sequences, capturing the motion of 398 unique individuals. In total, annotate 327,235 frames and produce 308,533 pose estimates. As shown in Figure E our dataset encompasses a broad spectrum limb deficiencies. As illustrated in Figure (a) the most prevalent single-site deficiencies are located above the left knee (16.74 (16.74%), below the left elbow (15.25%), and below the right knee (12.26%); Multi-site limb differences account for 16%72% of the population, highlighting the complexity of representation beyond isolated cases and the necessity of modeling diverse impairment patterns for robust performance. The co-occurrence matrix further demonstrates that upper- and lower-limb deficiencies frequently overlap across different body sides, reflecting realistic clinical patterns. The gender breakdown in (b) is nearly balanced (51% female, 49% male), ensuring that learned pose models do inherit a strong gender bias. Finally, the prosthetic-usage chart in (c) shows an even split between subjects with and without prostheses, indicating that the dataset equally represents natural residua and prosthetic-assister movements. Together, these statistics demonstrate that the dataset provides a representative and diverse sample of limb-deficiency conditions, gender, and assistive-device usage, which critical for developing generalizable human-pose estimation algorithms.
Data use and license.
The annotations and all website content for the Inclusive VidPose dataset 2025 Inclusive VidPose Consortium and are released under the Creative Commons BY-NC-SA 4.0 license We will continue maintain the dataset and release versioned updates with changelogs.
EXPERIMENTS
4.1 EXPERIMENT DETAILS
We benchmark our InclosiveViPPoee dataset on a single-frame pose estimation task. Single-frame evaluation highlights exactly where models struggle with residual-limb endpoints, guiding targeted improvements in keypoint localization and confidence calibration. As the first pose-estimation dataset dedicated to individuals with limb deficiencies, this image- based evaluation establishes clear baselines for residual-lime limb detection and ensures fair comparison across models by using identical single- frame inputs and metrics, while also informing future extensions multi-fram pose estimation.
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Table 2: Main experimental results on pose estimation algorithms. We evaluate models includ- ing Swin-based top-down heatmap networks (Liu et al. 2021a), ViTPose (Xu et al. 2022), and RTMPose (He et al. 2024), as well as the bottom-up model DEKR (Geng et al. 2021], J. the detector- based single-stage model FOLOX-PPsse (Maji et al. 2022), and the video-based ViPNAS (Xu et al., 2021). VnnclusiveVidPoon
- Inclusive VidPPos" reports training on our training set and evaluation on our validation and test splits. "COCO Inclusive VidPose" reports training only COCO and evaluation on our Inclusive VidPose validation and test splits. "Inclusive VidPose + COCO + Inclusive PoclusiveViPPoee/COOO VidPose/COCO" reports training on both datasets, validating on our validation split and testing on our test split and COCO validation. "COCO + COCO" reports COCO training and COCO validation performance (from MMPose).
Inclusive VidPose
InclusiveVidPPos + COCO + Inclusive VidlPos
COCO
Method
Backbone
Toput Size
+ IndlosseeVidPose
| COCO
+ COCO
AP APoo
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AP
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AP
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AP
AR
AP
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YoloxPose-T
416v416
60.1
75.3
63.9
73.3
86.0
76.0
70.7
74.1
88.0
41.7
46.9
52.6
YoloxPose
YoloxPose M
YoloxPose-S S 640x640
640xx640
65.7
65.4
802
80.5
09.9
70.0
77.5
76.8
89.1
90.5
80.7
80.1
69.9
72.7
78.4
79.5
93.3
91.4
74.0
73.6
53.7
82.3
77.0
27.3
81.4
86.5
95.6
95.0
99.8
90.6
78.0
72.0
59.4
49.1
74,5
54.2
64.1
69.5
89.9
68.2
YoloxPose-L
640x640
65.9
80.3
70.4
76.2
88.5
79.9
67.7
80.0
94.9
74.3
84.1
17.5
85.3
94.7
89.1
69.5
64.9
80.5
71.2
90.1
DEKR
3R
512x512
77.7
83.5
79.5
83.2
89.0
84.8
55.2
55.8
73.6
73.6
78.2
14.9
89.3
96.3
90.5
62.1
59.5
66.3
66.6
73.5
HRNot-w-88
640x640
75.2
85.1
67.3
72.1
71.4
ViPNAS
Mr
196x192
78.6
87.6
80.3
50.3
88.6
81.5
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77.22
72.9|81.3
55.6
83.3
83.2
89.8
84.3
55,0
(68.5
72.2
669.5
75.5
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156x192
81.2
89.6
83.1
82.6
90.0
84.5
68.8
72.6
62.0
58.6
84.4
83.5
80.9
85.3
68.9
(68.3
71.7
72.4
78.2
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Swin-R
256x192
78.8
87.5
88.4
67.8
68.6
73.7
79.4
Transformor
Swin-8
354x288
81.8
88.6
83.4
83.2
89.8
84.5
69.3
78.8
N2.7
94.3
85.4
74.6
70.6
75.9
81.1
Swinst
256x192
79.5
88.4
81.1
81.1
89.5
82.3
68.7
77.1
79.0
50.0
58.4
82.1
81.9
80.2
83.9
68.2
65.2
69.3
74.1
79.8
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80.7
88.6
82.5
82.0
89.4
83.6
72.1
78.4
81.4
53.5
88.6
83.5
82.8
89.2
84.5
72.7
66.0
70.7
76.3
81.4
RTMPose-T 256x192
75.2
83.1
76.2
76.4
84.9
77.9
65.3
66.5
68
8
50.2
89.7
82.5
81.6
90.5
83.7
73.1
67.66
70.9
668.2
73.6
RIMPose
RTMPose
RIMPose
156x192
256x192
79.3
84.55
77.3
83.3
50.7
89.9
86.4
69.5
94.1
71.8
74,0
53.6
83.4
85.6
84.8
96.3
71.4
72.2
75.1
74.6
71.6 76.8
RTMPose-L
256x192
82.2
89.7
83.5
83.2
90.1
84.1
69.1
76.2
76.
81.9
89.6
83.2
83.5
90.1
84.6
60.8
72.6
75.5
75.8
80.6
VIT-S
156x192
82.5
R9.7
84.4
84.1
90.7
85.7
67.1
72.5
75
84.2
93.7
85.0
70.6
70.2
73.7
73.9
79.2
VilPose
VIT-B
VIE-L
256x192
256x192
82.5 89.6
85.5 90.7
84.2
86.5
54.1
86.8
91.6
90.2
87.3
85.6
73.8
70.3
77.4
79.9
82.5
81.0
K3.3
56.
91.6
80.5
85.4
87.4
84.9
87.4
92.1
90.4
86.5
88.4
72.0
75.1
78.4
74.3
81.2
77.6
75.7
78.2
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VIT-H
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Beyond this image-based setting, we further build a video-based benchmark by adapting the Pose evaluation protocol to vaclusiveVidPosss We evaluate two representative multi-frame pose estimators, DCPose (Liu et al., 2021b) and DSTA (He & Yang, 2024). and report Pose -rack-sylle AP for standard joints and for our residual-limi groups. This video-based evaluation tests whether temporal aggregation helps track residual-limb endpoints across time and offers the first reference results for video pose estimation on individuals with limb deficiencies.
Specifically, we sample one frame every 60 frames from each video. We then split the dataset at the video level into training, validation and test splits in a ratio of 1 2. All sampled frames from the same video are kept together in one split. Therefore, no individual appears in more than one split to prevent any data leakage. Furthermore, we believe that a good pose estimator must serve all users rather than a single group. In light of this, we measure performance on both able-bodied and limb- difference anatomies. We train and evaluate models on COCO dataset alongside InclusiveVidPose under the same settings. This dual-dataset approach provides fair comparison of how different algorithms handle standard and disability-awe aware keypoint estimation. By benchmarking across both COCO and InclusiveVidPose, we ensure our evaluations drive improvements that benefit everyone, from able-bodied individuals to those with limb deficiencies. All models in Table 2 are initialized from their official COCO-pretrainee weights.
4.2 EVALUATION METRICS
COCO metric are used to evaluate overall pose estimation performance. We report average precision (AP) and average recall (AR) at OKS thresholds 0.50 and 0.75, i.e., AP, APS0, AP75, AR, 50 and
75
75 These metrics follow the COCO protocol and measure keypoint localization accuracy.
Pose Track metrics are used to evaluate video-based pose estimation Inclusive Following the PoseTrack protocol, we compute keypoint average precision (AP) over all frames and report AP for each joint category well the mean AP over all keypoints. On our benchmark, we further report AP for the four residual-lime groups (AmuUp, ArmLow, LegUp. LegLow), which aggregate our residual-limb endpoints and quantify localization performance on dislbel regions.
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Table 3: PoseSrack-style keypoint AP on Inclusive VidPose. We report AP (%) of DCPose (Liu et al. 2021b) and DSTA (He & Yang (222) on standard joints and residual-limb groups (AmmUp. ArmLow, LegUp, LegLow), together with the mean over all keypoints.
Head
Shoulder
Elhow
Wrst
Hip
Knce
ArmUpp
ArnLow
LegUp
LegLow
DCP
102
28.1
72.0
79.3
16
0.2
160
DSTA
Yang
28.9
70.4
81.9
72.7
71.9
72.0
0.0
14.3
17.3
Limb-specific Confidence Consistency (LiCC) is introduced measure whether a pose-estimation model can correctly distinguish intact limbs from residual or missing limbs. This capability is critical for assessing the inclusiveness of pose-estimation systems on data that includes individuals with limb deficiencies. Let V be the set of all ground-truth keypoints with visibility e 2 1, and for each keypoint:
V, denote by (i) the set of mutually exclusive keypoints. For example, if the residual wrist keypoint is visible, then both the residual elbow keypoint and the normal wrist landmark cannot be present. Denote by s; the predicted confidence for keypoint i, and by max 8j jEM(() the highest confidence among its mutually exclusive partners. LiCC is defined as the average fraction of keypoints whose confidence exceeds that of any exclusive keypoint:
LiCC = IV
Σ iEV
I(s:
max 83), JEM((()
where 1() is the indicator function. A higher LiCC indicates stronger consistency: visible keypoints assigned higher confidence than any impossible alternatives. Pseudocode for computing LiCC is provided in the supplementary materials.
4.3 BENCHMARK RESULTS
Single-frame pose estimation. As shown in Table 2 when train on InclusiveVidPor and evaluate on Inclusive VidPose, large backbones like HRNet-w48, Swin-L., and ViTPose (ViT-H/L. get higher AP scores on visible points. However, LiCC is still low for most methods, about 60% in many cases. This means many residual keypoints are predicted wrongly. Existing models assume that all keypoints exist, so they often predict high confidence for impossible points. DEKR and ViPNAS have good COCO AP but low LiCC, so they do not tell intact joints from residual endpoints well. In contrast, YOLOX- Pose, which uses confidence learning, gets higher LiCC scores because it separates intact and residual cases better.
To make the distribution shift between COCO and Inclusive explicit, Table also reports COCO-Inclusive VidPose setting. We train all models only on COCO and evaluate them on our validation and test splits, using a 17-keypoint version of Inclusive VidPose that keeps only the COCO joints and ignores the 8 residual endpoints. Compared with COCO-COCO under the same 17-keypoint label space, many methods show lower AP or AR and their ranking changes, which indicates that Vndlosi presents different and more challenging test distribution. At the same time, strong models such as ViTPose-H and YOLOX-Pose- still reach high AP and AR (e.g.. 81.5 AP/ 83.8 AR and 80.0 AP/ 0/9 94.9 AR in the IDCO-+Inclusive VindPss setting), which shows that COcO-trained models can transfer reasonably well to our 17 keypoint annotations. This behaviour is more consistent with a real distribution shift between COCO and InclusiveVidPose, combined with different generalization strengths across architectures, than with inconsistent keypoint annotations in our dataset.
Furthermore, we add COCO to training. This helps large backbones on Inclusive VidPose, but it hurts small models like YOLOX-Poser and RTMPose-T on people with limb differences. A fair comparison under fixed label space therefore compares COCO--Inclusiveeiin VidPose to COCO-COCO to measure dataset shift, and compares
VidPose Viniluus and VidPose understand what in-domain training achieve on the same 17 standard keypoints. +OCO->COCO also drops compared with a COCO-only baseline that predicts only 17 keypoints, because the head must now predict eight extra residual- lesmu keypoints. The output space is larger, and the capacity moves away from the original 17 points. Additional results on the endpoints are in Appendix $D including results on 8 endpoints and 17 keypoints.
Overall, LiCC is low across most methods. On Inclusive VidPose, many models are around 60% LiCC. In many frames, the model gives higher confidence an anatomically impossible point than

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Bradesco
Bradesto
Inclusive VidPose
Prediction
Inclusive VidPose
ViTPose Prediction
Figure 7: Case study of ViTPose (ViT-B) predictions trained on InclusiveVidPose We highlight incorrect keypoints and skeleton segments in bold, demonstrating the challenges introduced Inclu- siveVidPose Residual-limb endpoints appear in purple, and all other keypoints appear in green.
to the true residual point. This shows that current pose estimators do not handle missing altered limbs. Our dataset and the LiCC metric make this problem clear and give a target for better methods.
Case study. As demonstrated in Figure these results show that existing methods fail to generalize effectively to individuals with limb deficiencies. More than half of the visible keypoint instances exhibit conflicting predictions, underscoring the importance of our dataset for developing and evaluating algorithms that can handle anatomies with missing or altered limbs. These errors occur across activities and views, showing that InclusiveVidPool is challenging and calling for new, more inclusive models that can reason about absent joints and residual endpoints.
Multi-frame pose estimation. Table [ibble/repone reports PoseTrack-style keypoint AP InclssiveVidPose for two representative video pose estimators, DCPose and DSTA. Both models keep relatively high AP on standard joints: shoulders, elbows, wrists, hips, knees, and ankles all stay around the low mid 70s, and DSTA gives slightly higher scores than DCPose on most of these joints, especially the wrist. Head AP is much lower for both methods, around AP, which is consistent with frequent occlusion, cropping, and motion blur in vidcos.
In contrast, performance on the four residual-limi groups is very poor. For ArmUp and ArmLow, AP almost zero for both methods, and even for LegUp and LegLow, AP remains in the low to mid teens. The mean AP over all keypoints only increases from 43.2 for DCPose to 43.7 for DSTA, which shows that small gains on intact joints and lower limbs do not close the large gap residual-limb regions. These results indicate that state-of- off-the-art video pose estimators, which are mainly trained and tuned on intact-body datasets, do not transfer our residual-limb keypoints, even after we aggregate VidPose therefore defines a new evaluation regime where models need to reason about diverse amputation levels, prosthetic shapes, and asymmetric limb geometry. By making this failure mode measurable in a realistic video benchmark, our dataset supports both scientific progress toward more general human motion understanding and practical advances in inclusive analysis for rehabilitation, sports, and everyday activities of people with limb differences.
eight endpoints into four coarse groups. VidPose
5 CONCLUSION
In summary, we introduce VidPose, the first large-scale video-based human pose estimation dataset focused on individuals with limb deficiencies, addressing a critical gap left by existing benchmarks that only represent able-bodied anatomies. By capturing diverse anatomical variations with precise annotations, including novel residual-limb end keypoints and leveraging video context to disambiguate occlusions from absent limbs, our dataset enables more accurate modeling this underserved population. Our evaluation of state-of-the- handling missing or prosthetic limbs, motivating our proposed Limb-specific Confidence Consistency (LiCC) metric to better assess confidence calibration. We believe Inclusive VidPose and LiCC will drive the development of more inclusive and robust pose estimation algorithms, benefiting applications in rehabilitation, health assessment, and assistive technologies.
methods reveals their limitations in
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ETHICS STATEMENT
This work uses publicly available videos from YouTube and archival materials from the International Paralympic Committee (IPC), No new data are collected from human subjects, and no interventions are conducted. Use of YouTube content follows fair use in the United States and fair dealing for research and study in Australia (see $3.2). We obtained guidance from the institutional copyright officer and approval from the institutional Human Research Ethics Committee in September 2024, valid until September 2029.
We release annotations and documentation. We do redistribute YouTube videos. For You'ube content, we provide links and provenance only. For IPC materials, we are authorized to store and distribute the content for academic research. We remove content upon substantiated takedown requests. We retain only information already public on the source platforms (e.g.. upload date and user handle) for traceability.
This work aims to advance inclusive human pose estimation. By curating representative video data and releasing carefully documented annotations, we enable more reliable benchmarking and model development for individuals with limb deficiencies. The dataset supports fairer evaluation, fosters methodological innovation on non-standard anatomies, and facilitates downstream applications in rehabilitation analytics, accessible sports technology, and safety-critical monitoring. We expect these resources to help the community design models that better reflect human diversity and improve real-world accessibility.
We document known limitations, report disaggregated metrics where applicable ($4) and website content are released under CC BY-NC-SA 4.0 (see $3.5), and downstream users must follow applicable laws and institutional ethics requirements. The authors declare no conflicts of interest and no sponsorship that influenced the study.
Annotations
REPRODUCIBILITY STATEMENT
We aim to make all experiments reproducible. Data sourcing and licensing are described in $3.2 and $3.5 Dataset construction, annotation protocol, and quality controls are detailed in $3.4 Dataset statistics and splits are provided in $3.5 and $4.1 The benchmark tasks, metrics, and evaluation protocol appear in $4 We release an anonymized repository with training and evaluation scripts. configuration files, hyperparameters, model variants, seeds, and environment specifications at </> code
ACKNOWLEDGMENTS
This research funded part by ARC-Discovery grant (DP220100800 to XY and DP230101753) and ARC-DECRA grant (DE230100477 to XY). We thank Advance Queensland Industry Research Projects (AQIRP) for their support and guidance. We thank the area chair and anonymous reviewers for constructive comments.
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A STATEMENT ON LLM USAGE
We use a large language model only for language polishing. Its role is limited to grammar, spelling, punctuation, and minor wording edits. does not contribute research ideation, study design, data analysis, result interpretation, or substantive writing. All technical content and claims originate from the authors, and every edit receives author review and approval.
B DATASET WEBSITE
All information related to the dataset is available on our anonymous project website Inclusive Vid Pose
C BOARDER IMPACT
The Inclusive VidPose dataset and accompanying evaluation benchmark aim address critical gap in human pose estimation (HPE) research by centering on individuals with limb deficiencies, an underrepresented and clinically important population. We anticipate the following broader impacts:
C.I POSITIVE BENEFITS TO COMMUNITY
Fairness and Exclusivity in AI Development By providing large-scale, anatomically diverse video dataset, we encourage the community to move beyond able-bodied assumptions. This can catalyze new model architectures and training paradigms that generalize across wider spectrum of human bodies. helping to reduce algorithmic biases in downstream applications (e.g., surveillance and sports analytics).
Assistive Technologies and Rehabilitation Models trained and validated InclusiveVidPoss be integrated into physical-therapy monitoring tools, prosthetic calibration systems, and home-base based exercise feedback platforms for individuals with limb deficiencies. This integration enhances the independence and improves the quality of life for users with limb loss or congenital differences.
Research Resource The dataset includes rich annotations, i.e., keypoints, segmentation masks, bounding boxes, and tracking IDs. These annotations serve as valuable resource for developing novel pose estimation and biomechanics algorithms and for teaching best practices inclusive model evaluation.
We hope our work draws greater attention to individuals with disabilities and empowers them to benefit from advancements in technology.
C.2 POTENTIAL RISKS
Privacy and Misuse Although we only share YouTube URLs and anonymized keypoint data, improper downloading or re-identification attempts could compromise personal privacy. To mitigate this, we require strict anonymization standards and Data Use Agreement (DUA) that forbids deanonymization, re-identification, and commercial exploitation without consent.
Unintended Bias in Applications Models fine-tuned on limb-deticient data could be misapplied in contexts where missing-joii detection is erroneously interpreted injury non-compliance (e.g., automated safety monitoring). We recommend clear documentation of model limitations and ethical training for practitioners deploying these technologies.
D FURTHER ANALYSIS OF RESIDUAL ENDPOINTS
In Table 4
extend the study by reporting ViTPose-H on inclusiveVidPose with 8, 17, and 25 InclusiveVidPooe
keypoints under two training settings: Inclusive VidPose only and Inclusive InclusiveVidPose plus COCO, both evaluated on VidPose. Under InclusiveVidPose-nne VidPose-only training, AP the 17 standard joints
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Table 4: ViTPose-H results on VrelusiveVidPous for 8 residual endpoints and 17 standard joints. We report performance on three keypoint sets: the 8 residual endpoints, the standard 17 COCO joints, and the extended 25 25-keypoint schema. The left block InclusiveVidPoose -> mnclusiveVidPosse trains ViTPose-H from scratch on veclsiiveVddPos without COCO pretraining. The middle block (InclusiveVidPPl) +
initializes from COCO weights and fine-tunes only on Inclusive The right block (InclssveVVidPoee=CCOO VidPose+COCO - AP on the 17 standard keypoints
jointly trains on
COCO and IicPusiveVidPooe before evaluation on InclusiveVidPous consistently higher than AP on the 8 residual endpoints, which suggests that residual endpoints harder to localize. Adding COCO, either as initialization or as extra training data, mainly helps the 17-keypoint subset, while accuracy the 8 residual endpoints changes little.
MnchsiieVidPoce
InclosiveVidPose Inclusive
InclusiveVidPose Inclusive VOCC Inclusive
Keypoints
AP Ap50
AP75
AR
AR
AR70
7G
AP
Ap30
AP75
AR
AR
00
ART
AP
AP30
AP"G
AR
AR30
AR75
8
82.4
90.1
85.4
85.2
91.6
87.2
84,2
92.2
86.3
86.7
93.1
88.0
82.2
92.0
84.7
85.7
93.1
87.1
90.9
86.7
87.5
87.9
87.0
91.6
88.3
88.8
25
84,8
90.6
86.3
86.3
91.1
89.4
86.3
90.8
87.5
87.6
91.8
88.7
86.5
91.6
87,5
87.9
92.4
88.9
Table ViTPose-H performance on 17 COCO keypoints by limb-deficiency group. We report COCO-style AP and AR on VidusiveeidPoss using a 17-keypoint subset and group clips into Arm Left, Arm Right, Leg Left, Leg Right residual-limb cases and Intact clips, showing consistent annotation of the shared joints across limb-deficiency types.
AP
Ap50
AP75
AR
AR50
AR
Arm Left
84.7
88.4
85.9
86.6
89.9
87.4
Arm Right
82.3
86.1
82.9
86.2
89.7
86.8
Legg Leet
92.3
95.9
92.9
93.0
96.1
93.7
Leg Right
87.5
91.9
88.6
88.7
93.0
89.1
Intact
75.7
84.1
76.4
81.5
88.1
83.0
86.7, while AP on the residual endpoints 84.2, which suggests that the standard joints are easier. This is because most subjects have only one or two residual endpoints, their appearance varies across individuals, and occlusion near amputation sites common. When COCO is added to training, the main gains appear on the 17-joint subset (AP increases to 87.0 and AR to 88.3). The 8-endpoint subset does not improve (AP decreases to 82.2 and AR to 85.7), and the unified 25-keypoint set changes only slightly (AP moves from 86.3 to 86.5). These results suggest that large general datasets mainly strengthen common joints, while the residual endpoints still need targeted data and modeling.
To isolate the effect of COCO pretraining, we add an "inclusiveVidPPsss + Inclusive setting, where ViTPose-H is trained from scratch on Inclusive VndlusiveVidPose without any COCO initialization. Comparing this baseline with "Inclusive VidPose - Inclusive VidPose" shows that COCO pretraining brings consistent gains across all keypoint sets: AP/AR improve from 82.4/85.2 to 84.2/86.7 on the 8 residual endpoints, from 85.2/86.6 86.7/87.9 the 17 standard joints, and from 84.8/86.3 to 86.3/87.6 6 on the full 25-keypoint schema. The improvements are similar in magnitude for residual endpoints and standard joints, which suggests that pretraining on a large intact-body dataset provides a useful generic pose prior without harming residual-endpoint localization. Combined with the joint-training results, these trends indicate that COCO mainly acts stabilizes common body structure, while closing the remaining gap between standard joints and residual endpoints still requires targeted mnclusiveVidPos-- TidPose--tyle data and modeling.
strong initialization that
We further evaluate ViTPose-H on a 17-keypoint version of Inclusive VidPose and group clips into five categories: Arm Left, Arm Right, Leg Left, Leg Right, and Intact. shown in Table X, ViTPose-H achieves high AP and AR for all four limb-deficiency groups (around 82-85 AP for residual-arm clips and 87-92 AP for residual-leg clips), while the Intact group is noticeably lower (75.7 AP and 81.5 AR). This is expected because, for limb-deficiency clips, keypoints on the missing limb are marked with visibility = 0 and are ignored when COCO metrics are computed, so the evaluation only covers the visible standard joints. In contrast. intact clips include larger set of visible joints, including more occluded and fast-moving points that are harder to localize. The gap between arm and leg groups suggests that upper-limb joints near the torso remain challenging than lower-limb
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Figure 8: Failure cases of DSTA on InclusiveVidPose Each frame shows two rows: the top row visualizes the ground truth keypoints and the bottom row shows predictions from DSTA, All examples come from the Inclusive VidPose test split. Even when the overall pose looks roughly plausible, DSTA often places residual endpoints and nearby joints at anatomically implausible locations relative to the clearly shortened limbs and prostheses in the ground truth.
joints, but across all groups ViTPose-H maintains strong 17-keypoint performance. These results support that the shared COCO joints that the main difficulty lies in detecting and localizing residual endpoints rather than in inconsistent placement of standard keypoints.
are annotated consistently across limb-deficiency types and
E FURTHER ANALYSIS OF MULTI-FRAME POSE ESTIMATION
In the main paper, we evaluate both image- based pose estimators and recent multi-frame video models, including DCPose (Liu al., 2021b) and DSTA (He & Yang 2024). on Inclusive VidPose. For the video benchmark we follow Pose Track style evaluation protocol and report mAP over keypoints. All reported numbers use the same 25 keypoint skeleton, the same training split of InclusiveVidPoss
Head joints. The behavior of head keypoints on VndPoss differs from PoseTrack style benchmarks. Many IndloseveVidPose sequences center the torso and residual limbs, for example rehabilitation training or prosthesis demonstrations, so the head is often near the crop boundary or partially out of frame. In contrast, TrackTrack videos usually show full body, upright people with clear heads near the image center Andriluka et al.] (2018). In addition, facial keypoints are annotated in fewer instances than most torso and leg joints. Each head joint appears only on the order of one hundred thousand labeled instances in our video annotations, which is roughly about half of the supervision frequency that standard lower body joints receive. As a result, all models see noticeably fewer positive examples for the head than for knees, hips, and ankles during training, and head AP is lower than for better supported joints.
Residual endpoints and temporal assumptions. The larger gap on residual limb endpoints comes mainly from how current video pose models are designed. DCPose and DSTA are built to exploit temporal continuity in videos where every subject has a complete skeleton. They always predict a fixed set of joints for each person in every frame, and they aggregate neighboring frames to smooth short term occlusions, blur, or defocus (Liu et al. 20216] He & Yang 2024). On PoseTrack this assumption holds, because there limb deficiencies and occlusions usually temporary. In
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VaclusiveVidPosee residual limbs are structural rather than temporary. Many participants have only one or two residual endpoints and the rest of the limb is truly absent. When a model that assumes a full limb sees such sequence, tends to hallucinate complete arm or leg and gradually pull the residual endpoint toward a typical wrist or ankle location. Once this happens near the beginning of clip, the temporal module propagates and stabilizes the wrong configuration across many frames. Residual endpoints then follow a smooth but systematically shifted trajectory and receive low AP under per frame evaluation.
The image baselines instead make independent predictions at each frame. They still struggle near residual limbs, but early mistakes do not automatically propagate in time, so they can recover on frames where the stump is well visible. Consistent with this interpretation, intact joints such as knees and ankles remain in the 70+ AP range in our video benchmark, which close to what comparable architectures report on PoseTTrack under related settings (L.iu et al. 2021b He & Yang 2024
Failure cases on VidPos.. Figure illustrates typical failure cases of DSTA on Inclu- sive VidPose. these sequences the global pose is roughly plausible and the temporal aggregation produces visually coherent predictions across frames, yet the residual endpoints and nearby joints are often placed at anatomically implausible locations relative to the clearly shortened limbs and prostheses in the ground truth. These examples support our quantitative finding that existing multi frame architectures, which are tuned for intact body benchmarks and fixed skeletons, do not yet capture residual limb anatomy even when trained the same annotations. The fact that intact joints reach normal AP while residual endpoints remain much harder to localize reinforces our main claim that Inclusive provides demanding but realistic testhed for developing temporal pose models that explicitly represent limb deficiencies.
F DISCUSSION AND FUTURE WORK
Limitation Even with a careful multi-amnotator process and video, telling an occluded limb from an absent limb is sometimes unclear. This can add small noise to labels and make training bit harder. We also see that current pose models on our data sometimes give unreliable confidence for missing or prosthetic limbs. These points suggest clear next steps for better guidelines and better models.
Opportunities for prosthesis-aware modeling. Beyond benchmarking, our annotations are de- signed to support inclusive model design. First, while the current keypoint schema focuses on human anatomy and does not include prosthesis-tip joints, pixel-level masks and per-limb prosthesis status naturally enable future work that introduces prosthesis end-effectors for applications requiring precise contact modeling with the environment (e.g., gait analysis object manipulation). Second, the per-limb prosthesis status can be used by future pose estimators through conditional keypoint heads, adaptive skeleton graphs, or curriculum training schemes that explicitly condition on prosthesis presence.
Privacy-preserving synthetic data augmentation. In future work, will explore the of synthetic data to protect participant privacy while enriching the Inclusive VidPose dataset. Real- world videos may raise privacy and ethical concerns and can be difficult or impractical collect for rare limb-deficiency cases. Crucially, the success of any synthetic-date pipeline depends on high-quality base of real examples. In our case, our Inclusive VidPose corpus provides the anatomical diversity and motion variability needed to guide realistic synthesis. To overcome the scarcity of certain amputation scenarios, we will generate motion sequences of limb-deficient subjects by combining advanced human-body simulators with generative adversarial networks. These synthetic clips will span uncommon amputation levels, diverse prosthesis designs, and challenging viewpoints or occlusions. We will then integrate them with real footage. Finally, domain-adas adaptation and style-transfer techniques will harmonise appearance and temporal dynamics across synthetic and real data, ensuring that models trained the hybrid dataset generalize robustly to in-the-will wild videos.
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Algorithm 1 LiCC: Limb-specific Confidence Consistency
Require: Ground-truth annotations G, keypoint predictions D, number of keypoints K, rules
Ensure: LiCC score
c+00
correct comparisons D total comparisons
2:
+-0 N+0
3: for (g, d) in zip(G, D) do
4: gt 4 reshape -eeepooiiss K, 3) pred <- reshape (d. keypoints, K, 3) for all rule R do
5:
6:
7:
i rule.main kp idx
8:
C rule.compare kp indices
9:
8 t gt(i,
> ground-truth visibility
if E (1,2] then N + N +1
11:
m predli, 2
13:
mc - [prdd 2] E {
q -- mmx)
15:
if m > q then
c c
17:
end if
18:
end
19:
end for
20:
end for
21:
return
N N > 0 else 0
Mutlimodal data for individuals with limb deficiencies. While the current dataset focuses on RGB video frames with 2D keypoint and segmentation annotations, extending the data modalities could unlock richer biomechanical and temporal analyses. In particular, incorporating depth maps or multi-view camera streams would provide 3D pose ground truth and enable reconstruction based methods to handle occlusions more robustly. Additionally, fusing inertial measurement unit (IMU) data from wearable sensors could offer complementary kinematic signals for smoother tracking of residual limbs. Finally, exploring audio-visual cues, for example, gait-associated sounds or speech commands, may facilitate multimodal models that better understand the context and intent of individuals with limb deficiencies during natural activities.
G IMPLEMENTATION DETAILS
Experimental Sctup All experiments were run on workstation equipped with two NVIDIA RTX 4090 GPUs and an AMD Ryzen Threadripper PRO 3955WX CPU. The system software environment is Ubuntu 24.04.2 LTS, Python 3.11.11, PyTorch 2.5.1 with CUDA 12.4. Our imple- mentation based on the MMPose library }. For data preprocessing, augmentation, and training hyperparameters, used the default settings of each method provided by MMPose. All training configurations, including exact config files and command-line instructions, have been released in the InclusiveVidPoss
Limb-specific Confidence Consistency(LiCC) Pseudocode Explanation As shown in Algo- rithm [ we denote G as the set of all ground-truth annotations, D as the set of corresponding pose estimation results, K as the total number of keypoints, and as the collection of mucuaa-excllsson
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rules. In our case,
R =
(7, (177))), (9, (17, 19)), (17, (7,9)), (19, (9)),
Left Upper Extremity
(8, {188), (10, {18, 20)), (18, {8, 103), (20, {10)).
Right Upper Extremity
(13, (21)), (15, {21, 23)), (21, {13, 15)), (23, (15)),
Left Lower Extremity
(14, (22)), (16, (2,, 24)), (22, (14, 16)), (24, (16))
Right Lower Extremity
where each tuple (i. (i....) }) enforces that, whenever keypoint is visible in the ground truth, its predicted confidence must strictly exceed the maximum confidence among the mutually-exclusive indices fi.... }. The algorithm maintains two counters, c for correct comparisons and N for total comparisons. For each pair (g. d) E zi zip(G, D), we skip if is empty; otherwise we reshape g g.keypoints into gt R (visibility column 3) and d. keypoints into pred E R x3 extracting its third column the predicted confidence vector. We then iterate over every (i, C) E R: ensure i E [0, K), read visibility v = 2t, and if o E {1,2] increment N, set m = pred[i], gather {pred[j] j E C} into me, skip if empty, else let
max(mc) and if m > g increment Finally, we return c/N when N > 0, or 0 otherwise. This LiCC metric thus measures how consistently a visible keypoint's confidence surpasses that of all its mutually exclusive counterparts.
H TERMS OF USE
H.1 ANNOTATIONS AND WEBSITE
The annotations and all website content for the inclusiveVidPose dataset are (c) 2025 InclusiveVidPose Consortium, and are released under the Creative Commons BY-NC-SA 4.0 license C
H.2
INFRINGEMENT
If you believe any material in this dataset infringes your rights, please contact the authors. We will promptly review and remove the infringing content.
H.3 VIDEOS AND FRAMES
All videos referenced in this dataset are publicly available YouTube videos or YouTube Shorts. We do not host distribute the video files extracted frames, instead, each annotation record includes the corresponding publicly accessible YouTuube URL. Any use of these videos or frames (e.g., viewing, downloading, processing, redistributing) must fully comply with YouTube's Terms of Service (https: //tts:://ww www. yout ube, wtt.t/tteeeeeeeecte as well as any additional restrictions imposed by the original content creators channels. Downstream users assume sole responsibility for ensuring that their use of this material conforms to all applicable copyright, privacy, and publicity laws.
Under both Australian and U.S. copyright law, academic researchers may, under certain conditions. use publicly available media, including YouTube videos, for research and study. In Australia, the Copyright Act 1968 provides fair dealing exceptions that permit the use copyright material for research or study where the use is fair. In the United States, Section 107 of the Copyright Act codifies fair use and explicitly identifies scholarship and research as purposes that can qualify under this doctrine. Our own collection and internal use of source videos falls within these research- focused exceptions; however, this dataset only distributes derived annotations (e.g.. keypoints and segmentation masks), not the underlying video content. Users remain responsible for ensuring that any use of the source videos complies with applicable copyright law and the terms of service of the hosting platforms.
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H.4 ETHICS ADDENDUM
By accessing, downloading, or using the Inclusive dataset (the "Dataset"), any downstream user ("You") must agree and abide by the following additional ethical obligations. which supplement the Dataset's base license terms:
Anonymization Standards
You may not distribute, publish, otherwise reveal any video frames metadata in form that could identify any individual, minor or adult, either directly or by inference.
Prohibition on Re-Identification
You shall not attempt, by any means, to re-identify any person depicted the Dataset. You shall not augment the Dataset with external data or use deanonymization algorithms to recover or expose personal identities.
Non-Comen Com-Comerreial, Ethical Research Use Only
You shall the Dataset solely for non-commereial, rescarch-oriented activities that comply with all applicable laws, institutional review board (IRB) approvals or exemptions, and recognized ethical norms for human- -sujects research.
Any form of commercial exploitation, product development, or profit-driven application of the Dataset is expressly prohibited without the prior written consent of the Dataset's custodians.
Breach and Remedies
Violation of any provision in this Addendum constitutes a material breach of your rights use the Dataset, and may result in immediate termination of your license and access privileges. The Dataset custodians reserve the right to pursue any and all available legal and equitable remedies in the event of non-compliance.
H.5 DATA USE AGREEMENT (DUA)
As prerequisite for accessing the Inclusive dataset, all prospective users must complete a Data Use Agreement and submit for review. Each agreement will be examined by the dataset administrators ensure that proposed uses comply with ethical and legal guidelines. Only after the DUA has been approved will access credentials be issued. A scanned copy of the signed agreement is provided in the supplementary materials.
22


Reviews:
[{"review_id": "7mmVQX12xb", "reviewer": "Reviewer_yopn", "summary": "The paper introduces InclusiveVidPose, a large-scale video-based human pose estimation dataset focused on individuals with limb deficiencies. The paper argues that existing models are trained only on able-bodied individuals with full keypoints, which results in failing to generalize to this population. To address this, the authors have annotated a large amount of data with bounding boxes, segmentation masks, and keypoints. Additionally, the paper introduces an extended 25-keypoint schema for people with limb deficiencies, building on the 17 COCO standard. Lastly, the paper proposes LiCC, a percentage-based metric to evaluate how confident a model predicts limb deficiencies between mutually exclusive joints'. The paper presents a comprehensive set of experiments on recent HPE models and highlights the research gap this dataset addresses.", "questions": "1. As I mentioned above, some analysis of consistency/annotation quality seems to be necessary. Table 2 already provides an exhaustive overview of the results. However, adding a \"COCO -> InclusiveVidPose\" column and comparing it with \"COCO -> COCO\" can reveal the distribution shift between the two datasets. This analysis can also be broken down to limb deficiency types for more insight.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The problem is very well-motivated, and the paper addresses a clear gap in research. The paper presents all the tools necessary for future research in this domain, including a high-quality dataset, annotations, evaluation tools, and baselines.\n2. The dataset statistics show a well-balanced distribution of the population.\n3. The experimental evaluations are very comprehensive. The low LiCC score also supports the paper's claim about the necessity of this research.\n4. The paper is very well-written and uses appropriate words for its arguments and to report its findings.", "weaknesses": "1. The results show that by training on the introduced dataset, the performance of some of the models drops. The paper notes that this is due to the size of the models, larger output space, and their architectural optimization for the COCO dataset. However, this can also occur due to annotation inconsistencies across datasets. However, the paper lacks any quantitative evaluation of the annotation consistency. While the qualitative reports and supplementary materials include high-quality labels, additional consistency analysis would benefit the paper.\n2. There's a typo on line 515, where \"sp lits\" should be \"splits\"\n3. The lack of annotation for limb prosthesis end-joint coordinates is a minor weakness in this paper, as many HPE applications rely on how humans interact with their surroundings (e.g., walking, grabbing objects). Marking these end nodes as invisible in the annotations could hinder their performance.\n4. While the dataset labels the per-limb prosthesis status, it does not use it in the models. Still, this is not a significant weakness, and future work can build on it.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zzVaejTUdX", "reviewer": "Reviewer_uQTG", "summary": "This paper presents InclusiveVidPose, the first large-scale video-based human pose estimation (HPE) dataset designed specifically for individuals with limb deficiencies. The dataset contains 313 videos (≈327k frames) featuring 398 participants with various amputation and congenital limb-difference types. It introduces eight new “residual-limb end” keypoints extending the COCO 17-keypoint schema to better model missing or prosthetic limbs. Each frame is annotated with segmentation masks, bounding boxes, tracking IDs, and per-limb prosthesis status, verified under expert para-athletic supervision. The paper also proposes a new evaluation metric, Limb-specific Confidence Consistency (LiCC), which measures whether a model maintains coherent confidence calibration between existing and absent limbs. Extensive benchmarks with 12 existing pose models (e.g., ViTPose, Swin, HRNet, YOLOPose) reveal that current systems generalize poorly to limb-difference anatomies, often predicting anatomically implausible joints or over-confident scores on missing limbs.", "questions": "1) Since the dataset is video-based, have the authors tested temporal models like PoseTrack transformers or 3D CNNs? How does motion continuity affect missing-limb recognition?\n\n2) What happens if models are pre-trained on COCO and fine-tuned on InclusiveVidPose? Is catastrophic forgetting an issue for intact-body keypoints? Just curious about the outcome. \n\n3) Just wondering- could segmentation masks be used to guide pose estimation, e.g., via conditional keypoint visibility modeling?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) The dataset is well-curated and technically rigorous: (i) Video-based rather than static image design allows temporal disambiguation between occluded and absent limbs. (ii) Multi-modal annotation (pose, mask, prosthesis status, tracking) ensures reusability across research domains (pose, segmentation, tracking, rehabilitation). It will be a great contribution to the community. \n\n2) LiCC is a conceptually sound and practically useful metric to quantify anatomical plausibility and confidence consistency — an important step toward calibrated pose estimation models. It provides a tangible way to evaluate inclusiveness beyond accuracy. \n\n3) In general, the paper addresses a genuine fairness gap in pose estimation research, highlighting the underrepresentation of hundreds of millions of people with limb differences.", "weaknesses": "1) While the dataset and metric are valuable and they are good contributions, the methodological side (pose estimation models) remains entirely benchmark-based. The paper would benefit from proposing a baseline model explicitly optimized for inclusive anatomy (e.g., deformable skeleton graph, conditional keypoint masking). \n\n2) Despite the dataset being video-based, all experiments are single-frame. The paper defers temporal modeling to “future work,” missing an opportunity to demonstrate how temporal priors improve residual-limb estimation.\n\n3) Given the high variability in anatomy and camera conditions, annotation uncertainty modeling (e.g., probabilistic keypoints or confidence intervals) could be beneficial. The paper treats annotations as deterministic. That is potentially a good future work worth exploring further.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5i6e06EmwN", "reviewer": "Reviewer_22nG", "summary": "InclusiveVidPose introduces the first large scale video based human pose estimation dataset focused on individuals with limb deficiencies, addressing the lack of representation in existing benchmarks which assume anatomically intact bodies . The dataset contains 313 videos totaling 327k frames featuring nearly 400 participants with amputations, congenital limb differences, and prostheses, and it adds eight residual limb end keypoints to accurately model altered anatomy . Each frame includes pose keypoints, segmentation masks, bounding boxes, subject tracking, and per limb prosthesis status collected with expert guidance. Experiments show that state of the art human pose estimation models frequently predict anatomically impossible joints in the presence of missing or prosthetic limbs, motivating a new evaluation metric called Limb specific Confidence Consistency (LiCC) to measure confidence calibration under anatomical absence. InclusiveVidPose provides a challenging benchmark intended to drive research toward inclusive and robust pose estimation for rehabilitation, health assessment, and assistive technologies.", "questions": "- At line 309, the “annotation team training” field is empty. Is there any missing information?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- The paper presents a dataset of approximately 327,000 video frames featuring various types of limb deficiencies. It also introduces a new metric, called Limb-specific Confidence Consistency (LiCC), designed to help evaluate whether pose-estimation models can tell the difference between intact limbs and those that are residual or absent.\n- As shown in Appendix D, the AP scores on residual endpoints are lower than on standard joints. Moreover, while adding COCO during training enhances performance on standard joints, it deteriorates performance on residual limbs. This highlights a research gap in how to effectively combine the two without harming performance on either group, suggesting a valuable direction for future work.\n- The proposed dataset is in a video modality, which is crucial for individuals with amputations because per-frame information can help disambiguate occluded limbs from truly absent ones. Current 2D pose baselines rely on image-based datasets and often perform poorly on metrics such as LiCC. This suggests that models incorporating video modality are needed to reduce such ambiguity.\n- A comprehensive evaluation of the dataset is conducted using recent 2D pose estimators, including YOLOX-Pose, DEKR, ViPNAS, Swin Transformer, RTMPose, and ViTPose, to analyze their behavior on the proposed dataset using COCO metrics in addition to the newly introduced LiCC metric. Furthermore, parallel experiments are performed to examine how incorporating COCO into training affects performance on both the COCO and InclusiveVidPose datasets.", "weaknesses": "- Although the paper highlights the ambiguity between occluded and absent limbs when annotating data, where annotators use temporal cues to disambiguate, the baseline comparisons are all image based. It would have been beneficial if a simple video baseline had been introduced to demonstrate how temporal cues can help the model resolve this ambiguity and how future work could further improve video based methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)