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arxiv: 2604.28025 · v1 · submitted 2026-04-30 · 💻 cs.CV

ResiHMR: Residual-Limb Aware Single-Image 3D Human Mesh Recovery for Individuals with Limb Loss

Jiaying Ying , Heming Du , Kaihao Zhang , Sean M. Tweedy , Xin Yu This is my paper

Pith reviewed 2026-05-07 07:49 UTC · model grok-4.3

classification 💻 cs.CV
keywords human mesh recovery3D human modelingresidual limb reconstructionlimb losstopology adaptationsingle-image reconstructioncomputer visionSMPL model
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The pith

ResiHMR reconstructs 3D human meshes from single images for people with limb loss by explicitly modeling residual limbs and adapting the mesh topology to the observed anatomy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Standard single-image human mesh recovery methods assume people have complete limbs and produce poor results or invalid shapes when limbs are missing. The ResiHMR framework overcomes this limitation through residual-limb keypoint detection combined with a topology-adaptive optimization module that only considers the present body structure and a geometry module that adds realistic boundaries and terminations for residual limbs. This produces meshes that better reflect actual anatomy instead of forcing an intact-limb model. The method shows measurable improvements in joint position errors on both standard and residual parts when tested on real images of individuals with limb loss. Such capability matters for applications like prosthetic design, rehabilitation monitoring, and inclusive virtual environments.

Core claim

The paper presents ResiHMR as the first single-image 3D human mesh recovery system that explicitly reconstructs residual-limb surfaces and performs topology-adaptive optimization for individuals with limb loss. It uses residual-limb keypoints to drive a Residual Anchor-Factor Optimization that constrains the estimation to the observed kinematic subgraph of valid structures, and a geometry-based module to estimate residual-limb boundaries and convex termination. This contrasts with joint-removal approaches in fixed topologies by aligning the reconstruction directly with limb-loss topology, which better matches prosthetic biomechanics. Experiments on a curated dataset demonstrate reduced 2D MP

What carries the argument

Topology-adaptive Residual Anchor-Factor Optimization and geometry-based Residual-Limb Reconstruction, which together allow the model to handle non-standard limb anatomy by optimizing only observed parts and adding explicit termination geometry.

If this is right

  • Reconstruction quality improves under SMPLify-X and HSMR backbones on limb-loss images.
  • Residual-limb 2D MPJPE drops significantly compared to prior methods.
  • The output meshes align better with real-world prosthetic use and biomechanics.
  • Supports applications in rehabilitation and human-computer interaction for diverse body types.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Extending this approach to multi-view or video inputs could enable tracking of prosthetic movement over time.
  • Integration with medical data might allow personalized mesh models for surgical planning or custom prosthetics.
  • Addressing limb loss in mesh recovery reduces systematic bias in AI systems trained mostly on able-bodied data.

Load-bearing premise

ResiHMR depends on being able to accurately find the endpoints of residual limbs in ordinary photos and to calculate their shape without extra data collected specifically from people with limb loss.

What would settle it

Running the system on new photos of people with limb loss and comparing the output meshes to actual 3D scans of those same people; if the residual parts do not match closely or errors are larger than before, the approach would not hold.

Figures

Figures reproduced from arXiv: 2604.28025 by Heming Du, Jiaying Ying, Kaihao Zhang, Sean M. Tweedy, Xin Yu.

Figure 1
Figure 1. Figure 1: Demonstration of ResiHMR. Our framework recovers anatomically coherent 3D body meshes from a single RGB image by adapting the kinematic topology and explicitly reconstructing residual-limb geometry. Abstract Single-image human mesh recovery provides a com￾pact 3D, person-centric representation that supports anal￾ysis, animation, AR and VR, rehabilitation, and hu￾man–computer interaction. However, prevailin… view at source ↗
Figure 2
Figure 2. Figure 2: Failure cases of existing HMR methods on individ￾uals with limb loss. SMPLify-X and HSMR hallucinate intact limbs or distort the lower body due to their intact-limb priors and fixed kinematic topologies. In contrast, ResiHMR correctly lo￾calizes the residual-limb, avoids compensatory distortions, and re￾constructs an anatomically coherent body mesh. 1. Introduction Single image human mesh recovery provides… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of our ResiHMR framework.Given an input image, SMPL-X is initialized using intact 2D keypoints. Our Residual Anchor–Factor Optimization adapts the kinematic graph by refining anchor joints and residual-limb proportions under supervision of residual-limb 2D keypoints. The Residual-Limb Reconstruction module then removes distal limb geometry and generates a smooth, watertight stump surface, producin… view at source ↗
Figure 4
Figure 4. Figure 4: Dataset Demonstration of the proposed LDPose-LimbLoss Evaluation Dataset. Representative samples illustrating the diversity of subjects, amputation levels, poses, activities, and environments included in the dataset. Green overlays indicate the manually annotated body masks used to isolate the human body region. \hat {\mathbf {n}} = \frac {\mathbf {J}_a^\star - \mathbf {J}_t^{\text {init}}}{\left \|\mathbf… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative Evaluation of ResiHMR. For each input example, we show: (a) the input image, (b) the overlay of SMPL-X in the input view, (c) front view, (d) back view, (e) side view, (f) T-Pose view with model output Θ = {β, ψ, R, t, λr} and mr = { mk | vk = (xk, yk, zk, mk) ∈ Vr }, and (c) (d) (e) and (f) all have residual limb being highlighted in red view at source ↗
Figure 6
Figure 6. Figure 6: A visual comparison of AJAHR and ResiHMR (ours). (a,c) are copied from the AJAHR paper [8], where limb loss is represented by collapsing SMPL vertices toward the parent joint, resulting in joint-level truncation. (b) shows expert-verified evidence and an additional real image of the same individual revealing a clear below-knee residual limb, indicating that joint-level collapse fails to match the true anat… view at source ↗
Figure 7
Figure 7. Figure 7: Key Statistics of our LDPose-LimbLoss Evaluation Dataset. (a) Distribution of residual-limb types, covering upper- and lower-limb amputations across both sides of the body. (b) Gender distribution. (c) Ethnic composition of the participants. Together, these statistics demonstrate the dataset’s demographic and impairment diversity, providing a representative foundation for benchmarking residual-limb–aware 2… view at source ↗
Figure 8
Figure 8. Figure 8: Limitation: Residual-limb surface shape approxima￾tion. Although ResiHMR accurately localizes the residual-limb endpoint, the reconstructed stump surface adopts a smooth, con￾vex prior that does not fully reflect the subject’s true residual￾limb contour (yellow). The intact limb is reconstructed faith￾fully (green), indicating that the discrepancy arises from lim￾ited instance-specific stump shape cues rat… view at source ↗
Figure 9
Figure 9. Figure 9: Limitation: Depth ambiguity from monocular input. Although the mesh aligns well with the input image in the front view (a–c), the side views (d–e) reveal depth ambiguities inherent to single-image reconstruction. Specifically, the arms are positioned in front of the torso rather than resting on the hips, and the upper body exhibits forward flexion that is not visible from the frontal perspective. These dis… view at source ↗
Figure 10
Figure 10. Figure 10: Overview of our ResiHMR Framework with other existing HMR methods to initialize the SMPL-X/SMPL, in this case, we have HSMR as the exmaple for the regression-based HMR method. First, while ResiHMR can reliably estimate continuous residual-limb endpoints for the majority of typical amputa￾tion cases, particularly when the residual and intact limbs share broadly similar geometric structure, it cannot yet ca… view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative Comparison of ResiHMR with SOTA HMR methods. Please see this in GIF format in project page ‡ ResiHMR view at source ↗
Figure 12
Figure 12. Figure 12: More Qualitative Evaluation of ResiHMR. For each input example, we show: (a) the input image, (b) the overlay of SMPL-X in the input view, (c) front view, (d) back view, (e) side view, (f) T-Pose view with model output Θ = {β, ψ, R, t, λr} and mr = { mk | vk = (xk, yk, zk, mk) ∈ Vr }, and (c) (d) (e) and (f) all have residual limb being highlighted in red view at source ↗
Figure 13
Figure 13. Figure 13: More Qualitative Evaluation of ResiHMR. For each input example, we show: (a) the input image, (b) the overlay of SMPL-X in the input view, (c) front view, (d) back view, (e) side view, (f) T-Pose view with model output Θ = {β, ψ, R, t, λr} and mr = { mk | vk = (xk, yk, zk, mk) ∈ Vr }, and (c) (d) (e) and (f) all have residual limb being highlighted in red view at source ↗
read the original abstract

Single-image human mesh recovery provides a compact 3D, person-centric representation that supports analysis, animation, AR and VR, rehabilitation, and human-computer interaction. However, prevailing systems impose an intact-limb prior and degrade on people with limb loss, because fixed-topology models cannot represent residual limbs. In this work, we present ResiHMR, a residual-limb aware framework for single-image 3D human modeling. ResiHMR adopts residual-limb keypoints and introduces two components: (i) a topology-adaptive Residual Anchor-Factor Optimization module that constrains estimation to the observed kinematic subgraph of anatomically valid structures, and (ii) a geometry-based Residual-Limb Reconstruction module that estimates residual-limb boundaries and convex limb-termination geometry. These components introduce topology-aware optimization and explicit termination geometry as tools for human mesh recovery under non-standard limb anatomy. Unlike joint-removal methods in a fixed topology, ResiHMR explicitly reconstructs residual-limb surfaces and aligns optimization with limb-loss topology, which better matches prosthetic biomechanics and real-world use. To the best of our knowledge, this is the first single-image HMR system that explicitly reconstructs residual-limb surfaces and performs topology-adaptive optimization for individuals with limb loss. On a curated dataset of real-world images with limb loss, ResiHMR improves reconstruction quality under both SMPLify-X and HSMR backbones, reducing intact-joint 2D MPJPE from 41.32 to 37.40 with SMPLify-X and residual-limb 2D MPJPE from 73.61 to 23.19 with HSMR.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces ResiHMR, a single-image 3D human mesh recovery framework for individuals with limb loss. It augments standard backbones (SMPLify-X, HSMR) with residual-limb keypoints, a topology-adaptive Residual Anchor-Factor Optimization module that restricts estimation to the observed valid kinematic subgraph, and a geometry-based Residual-Limb Reconstruction module that estimates boundaries and convex limb-termination geometry. The authors claim this is the first such system to explicitly reconstruct residual-limb surfaces and align optimization with limb-loss topology. On a curated real-world dataset, it reports MPJPE reductions (intact-joint 2D MPJPE 41.32→37.40 with SMPLify-X; residual-limb 2D MPJPE 73.61→23.19 with HSMR).

Significance. If the results hold after addressing robustness concerns, the work would be significant for extending HMR to an underrepresented population with direct applications in rehabilitation, prosthetics, AR/VR, and inclusive HCI. The explicit residual-limb surface reconstruction and topology adaptation go beyond joint-removal heuristics in fixed-topology models and could better match prosthetic biomechanics. The quantitative gains on real images are promising, and the parameter-free motivation for the geometry module (when it works) is a conceptual strength.

major comments (3)
  1. [§3.2] §3.2 (Geometry-based Residual-Limb Reconstruction): The module relies on an explicit convexity prior for limb-termination geometry to estimate boundaries and produce surfaces without limb-loss-specific training data. Real residual limbs frequently exhibit non-convex terminations (surgical flaps, atrophy, bulbous ends). The manuscript contains no failure-case analysis, no per-shape breakdown of results, and no robustness tests on non-convex examples. Because the topology-adaptive optimization depends on accurate boundary estimates to select the valid kinematic subgraph, violation of the convexity assumption is load-bearing for both reconstruction quality and the central claim of reliable single-image modeling for limb loss.
  2. [§5] §5 (Experiments) and abstract: The reported metric improvements are presented on a 'curated dataset' without details on dataset size, limb-loss type distribution, curation criteria, ground-truth acquisition method, or statistical significance testing. The large residual-limb MPJPE drop (73.61→23.19) is therefore difficult to interpret or reproduce, weakening evidential support for the claimed superiority over baselines.
  3. [§3.1] §3.1 (Residual Anchor-Factor Optimization): The module introduces the free parameter 'Residual Anchor-Factor' whose sensitivity is not analyzed. No ablation varying this factor and reporting effects on MPJPE or topology selection is provided, leaving open whether the reported gains are robust or depend on careful tuning of this parameter.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'to the best of our knowledge' is strong; the introduction should include a concise comparison table or paragraph explicitly contrasting ResiHMR against the closest prior topology-adaptive or amputee-specific HMR methods.
  2. [Figures] Figures: Captions for qualitative results should explicitly label which images show non-convex residual limbs (if any) and whether the method succeeds or fails on them.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which identify important areas for strengthening the manuscript's clarity, robustness, and evidential support. We respond point-by-point below and will make the indicated revisions in the next version.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Geometry-based Residual-Limb Reconstruction): The module relies on an explicit convexity prior for limb-termination geometry to estimate boundaries and produce surfaces without limb-loss-specific training data. Real residual limbs frequently exhibit non-convex terminations (surgical flaps, atrophy, bulbous ends). The manuscript contains no failure-case analysis, no per-shape breakdown of results, and no robustness tests on non-convex examples. Because the topology-adaptive optimization depends on accurate boundary estimates to select the valid kinematic subgraph, violation of the convexity assumption is load-bearing for both reconstruction quality and the central claim of reliable single-image modeling for limb loss.

    Authors: We appreciate the referee's identification of the convexity prior as a key limitation in the Geometry-based Residual-Limb Reconstruction module. The manuscript explicitly adopts this prior to enable parameter-free boundary estimation and surface reconstruction without limb-loss-specific training data. We acknowledge that real residual limbs often exhibit non-convex terminations due to surgical flaps, atrophy, or bulbous ends, and that the current version lacks failure-case analysis, per-shape result breakdowns, or robustness tests on non-convex examples. This is a valid concern, as inaccurate boundary estimates directly affect the topology-adaptive optimization's selection of the valid kinematic subgraph and thus the central claim of reliable modeling for limb loss. In the revised manuscript, we will add a dedicated Limitations subsection that discusses the convexity assumption, its applicability to common post-amputation geometries, and potential failure modes. We will include qualitative examples of non-convex cases (drawn from additional real-world images or illustrative diagrams) showing where the module produces approximate terminations, and we will clarify the conditions under which the approach remains reliable. While we cannot add extensive new quantitative tests without additional data collection, this revision will transparently address the load-bearing dependency and temper the central claim accordingly. revision: partial

  2. Referee: [§5] §5 (Experiments) and abstract: The reported metric improvements are presented on a 'curated dataset' without details on dataset size, limb-loss type distribution, curation criteria, ground-truth acquisition method, or statistical significance testing. The large residual-limb MPJPE drop (73.61→23.19) is therefore difficult to interpret or reproduce, weakening evidential support for the claimed superiority over baselines.

    Authors: We agree that the description of the curated dataset in §5 and the abstract is insufficient, rendering the quantitative results difficult to interpret or reproduce. The manuscript currently provides only high-level references to improvements on real-world images with limb loss. In the revised version, we will expand §5 with a comprehensive Dataset subsection detailing: the total number of images and subjects; the distribution of limb-loss types (e.g., transtibial, transfemoral, upper extremity); curation criteria (e.g., selection from public repositories with visible residual limbs, diverse poses/viewpoints, and exclusion of heavy occlusions); the ground-truth acquisition method (expert 2D keypoint annotation with 3D lifting where feasible); and statistical significance testing (e.g., paired t-tests or Wilcoxon signed-rank tests with p-values and confidence intervals on the MPJPE metrics). We will also explain the large residual-limb MPJPE reduction as arising from the baselines' tendency to hallucinate joints on missing limbs, whereas ResiHMR's topology adaptation correctly restricts estimation to observed structures. These additions will make the results reproducible and strengthen evidential support for the claimed improvements. revision: yes

  3. Referee: [§3.1] §3.1 (Residual Anchor-Factor Optimization): The module introduces the free parameter 'Residual Anchor-Factor' whose sensitivity is not analyzed. No ablation varying this factor and reporting effects on MPJPE or topology selection is provided, leaving open whether the reported gains are robust or depend on careful tuning of this parameter.

    Authors: We concur that the manuscript lacks a sensitivity analysis for the Residual Anchor-Factor parameter introduced in the Residual Anchor-Factor Optimization module. This free parameter controls anchoring strength during topology-adaptive optimization but is not ablated. In the revised manuscript, we will add an ablation study to §5 that varies the Residual Anchor-Factor over a range of values (e.g., 0.1, 0.5, 1.0, 2.0, 5.0) and reports the resulting effects on intact-joint 2D MPJPE, residual-limb 2D MPJPE, and topology selection accuracy (percentage of correctly identified valid kinematic subgraphs). This will demonstrate the robustness of the reported gains within a stable operating range, justify the default value used in the main experiments, and directly address concerns about potential dependence on careful tuning. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces two new modules (topology-adaptive Residual Anchor-Factor Optimization and geometry-based Residual-Limb Reconstruction) as independent contributions to address fixed-topology limitations in existing HMR systems. These are motivated by the problem statement and evaluated via quantitative improvements on a curated limb-loss dataset against external backbones (SMPLify-X, HSMR). No equations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or description. The convexity prior is an explicit design choice for the geometry module rather than a hidden tautology. The derivation remains self-contained and does not reduce to its inputs by construction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard computer vision assumptions about keypoint detection and mesh optimization, plus the new modules introduced without detailed parameter counts in the abstract.

free parameters (1)
  • Residual Anchor-Factor
    Parameters used in the topology-adaptive optimization module to constrain estimation to observed kinematic subgraph.
axioms (1)
  • domain assumption Single images contain sufficient information to estimate residual-limb keypoints and geometry for mesh recovery.
    This underpins the entire framework as it relies on single-image input.

pith-pipeline@v0.9.0 · 5622 in / 1318 out tokens · 86548 ms · 2026-05-07T07:49:00.547487+00:00 · methodology

discussion (0)

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