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arxiv: 2604.27583 · v1 · submitted 2026-04-30 · 🧬 q-bio.NC · cs.RO

Recognition: unknown

Simulating Infant First-Person Sensorimotor Experience via Motion Retargeting from Babies to Humanoids

Francisco M. L\'opez , Hoshinori Kanazawa , Ondrej Fiala , Yakov Balashov , Valentin Marcel , Lukas Rustler , Miles Lenz , Dongmin Kim
show 3 more authors
Yasuo Kuniyoshi Jochen Triesch Matej Hoffmann
Authors on Pith no claims yet

Pith reviewed 2026-05-07 09:50 UTC · model grok-4.3

classification 🧬 q-bio.NC cs.RO
keywords infant sensorimotor experiencemotion retargeting3D pose estimationhumanoid simulationmultimodal sensor streamsdevelopmental analysisbehavior annotation
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The pith

A framework extracts 3D infant body poses from single videos and retargets the motions onto humanoid embodiments to produce simulated multimodal sensor streams.

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

The paper develops a technique to simulate what infants sense as they move by using video recordings of their actions. It first determines the 3D positions of the infant's body parts in each frame of the video through skeletal extraction and pose estimation. These positions are then transferred to the bodies of humanoid platforms. The transferred motions generate artificial sensory information from the platforms' joints, muscles, touch sensors, and cameras. This creates a detailed record of the experiences that can be studied to understand normal and atypical development.

Core claim

From a single video, the method reconstructs the infant's body configuration by extracting its skeletal structure and estimating the full 3D pose from each frame. Then we map the reconstructed motion onto several developmental platforms. Replaying the retargeted motions on these embodiments produces simulated multisensory streams including proprioception (joints and muscles), touch, and vision. For the best-matching embodiment, the retargeting achieves sub-centimeter accuracy and enables a rich multimodal analysis of infant development as well as enhanced automated annotation of behaviors.

What carries the argument

The motion retargeting pipeline that extracts skeletal structure and 3D poses from infant videos then maps them to humanoid embodiments to generate corresponding proprioceptive, tactile, and visual data streams.

If this is right

  • Reconstructs full 3D infant body configurations and poses from single videos via skeletal extraction.
  • Maps the motions to produce simulated proprioception, touch, and vision streams on target embodiments.
  • Achieves sub-centimeter accuracy when the embodiment matches the infant scale well.
  • Enables rich multimodal analysis of infant development from the generated data.
  • Supports enhanced automated annotation of infant behaviors in the source videos.

Where Pith is reading between the lines

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

  • The simulated streams could serve as standardized test cases for studying how differences in body size alter the interpretation of the same movement patterns.
  • Researchers might extend the pipeline to generate large datasets for training models that detect early deviations in sensorimotor patterns.
  • Varying the target embodiment parameters could reveal which aspects of body design most influence the fidelity of the simulated infant experience.

Load-bearing premise

Retargeting motions from infant bodies onto differently proportioned humanoid bodies with different joint limits and sensor placements still produces sensor streams that meaningfully approximate the infant's actual proprioceptive, tactile, and visual experiences.

What would settle it

Direct comparison of the simulated sensor streams against any available ground-truth sensor recordings from infants performing identical movements, or demonstration that retargeting error exceeds sub-centimeter levels across a broad set of infant videos.

Figures

Figures reproduced from arXiv: 2604.27583 by Dongmin Kim, Francisco M. L\'opez, Hoshinori Kanazawa, Jochen Triesch, Lukas Rustler, Matej Hoffmann, Miles Lenz, Ondrej Fiala, Valentin Marcel, Yakov Balashov, Yasuo Kuniyoshi.

Figure 1
Figure 1. Figure 1 view at source ↗
Figure 2
Figure 2. Figure 2: Motion retargeting pipeline. From a single view of a moving infant, the three dimensional body pose can be estimated and reconstructed in a humanoid. This method allows the simulation of multimodal sensory streams from a first-person perspective. The examples show EMFANT’s muscle-tendon structure, MIMo’s virtual skin of touch sensors activated due to a hand-to-body contact, and MIMo’s binocular vision when… view at source ↗
Figure 3
Figure 3. Figure 3: Accuracy of the motion retargeting. MIMo and EMFANT achieve the lowest relative distances, since their morphology can be modified to fit that of the recorded infant. The relative orientation and velocities, measured using vectors pointing from the MidHip keypoint to each end effector, provide a more qualitative comparison of the retargeting accuracy. MIMo performs best overall, with an average MAE below 2 … view at source ↗
Figure 4
Figure 4. Figure 4: Cross-embodiment invariance and shared sensorimotor man￾ifold. (A) Similarity triangle diagram summarizing pairwise latent correla￾tions among the three embodiments (EMFANT, MIMo, and iCub). Edge labels indicate Spearman ρ correlations. (B) Kernel density plot from dimensionality reduction of GPA-aligned fused latent distribution (tactile + proprioception + vision) at K = 20 view at source ↗
Figure 5
Figure 5. Figure 5: B. It consists of a sparse raster plot where only some view at source ↗
Figure 5
Figure 5. Figure 5: Simulation of sensorimotor infant experiences for different humanoids. (A) The robot iCub performing hand regard. His eyes have cameras which allow us to simulate the visual experience of an infant looking at their hand. Due to the proximity of the hand to the face, there is a binocular disparity in the left and right images. This is shown as the red and cyan colors in the stereoscopic anaglyph. (B) Touch … view at source ↗
Figure 6
Figure 6. Figure 6: Distributions of self-touches. The manual coding was performed by expert annotators, whereas the humanoid touches were detected as collisions between the hands and the bodies. been shown that these distributions of touches change during development and are indicative of a maturing body schema [36]. Following a similar approach, we extract the hand-to￾body contacts detected as collisions in the three simula… view at source ↗
read the original abstract

Motion retargeting from humans to human-like artificial agents is becoming increasingly important as humanoid robots grow more capable. However, most existing approaches focus only on reproducing kinematics and ignore the rich sensorimotor experience associated with human movement. In this work, we present a framework for simulating the multimodal sensorimotor experiences of infants using physical and virtual humanoids. From a single video, our method reconstructs the infant's body configuration by extracting its skeletal structure and estimating the full 3D pose from each frame. Then we map the reconstructed motion onto several developmental platforms: the physical iCub robot and the virtual simulators pyCub, EMFANT and MIMo. Replaying the retargeted motions on these embodiments produces simulated multisensory streams including proprioception (joints and muscles), touch, and vision. For the best-matching embodiment, the retargeting achieves sub-centimeter accuracy and enables a rich multimodal analysis of infant development as well as enhanced automated annotation of behaviors. This framework provides a unique window into the infant's sensorimotor experience, offering new tools for robotics, developmental science, and early detection of neurodevelopmental disorders. The code is available at https://github.com/ctu-vras/motion-retargeting/.

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 presents a framework that extracts skeletal structure and estimates 3D pose from single videos of infants, then retargets the motion onto physical (iCub) and virtual (pyCub, EMFANT, MIMo) humanoid platforms to generate simulated multisensory streams (proprioception, touch, vision). It claims sub-centimeter kinematic accuracy for the best-matching embodiment and positions the approach as enabling rich multimodal analysis of infant development and early neurodevelopmental disorder detection, with code released publicly.

Significance. If the retargeted streams were shown to preserve functional sensorimotor consequences under the target embodiments' geometry and sensor layouts, the work would supply a reproducible tool for generating otherwise inaccessible first-person infant data, with direct value for developmental robotics and computational modeling of early cognition. The public code release strengthens the potential impact by supporting reproducibility.

major comments (3)
  1. [Abstract] Abstract: the assertion of 'sub-centimeter accuracy' for the best-matching embodiment supplies no error distributions, measurement protocol, baseline comparisons, or validation against ground-truth infant motion-capture data, leaving the central kinematic claim unsupported.
  2. [Methods] Methods (retargeting pipeline): body scaling and joint-mapping parameters are treated as free parameters yet no procedure is given for selecting or validating them so that the resulting proprioceptive, tactile, and visual streams remain functionally equivalent to infant experience despite large differences in body proportions and sensor placement.
  3. [Results] Results: no sensitivity analysis or quantitative comparison of derived quantities (joint-torque distributions, contact-force ranges, visual-field coverage) against published infant literature values is reported, so the claim that the streams 'enable a rich multimodal analysis' rests only on kinematic reproduction.
minor comments (2)
  1. [Abstract] The abstract and introduction could more explicitly distinguish kinematic retargeting accuracy from experiential fidelity.
  2. [Figures] Figure captions should state the exact embodiment used for each accuracy number and whether the metric is end-effector or joint-space error.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for their constructive and detailed comments, which help clarify the strengths and limitations of our work. We address each major comment point by point below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion of 'sub-centimeter accuracy' for the best-matching embodiment supplies no error distributions, measurement protocol, baseline comparisons, or validation against ground-truth infant motion-capture data, leaving the central kinematic claim unsupported.

    Authors: We acknowledge that the abstract's kinematic claim would benefit from additional supporting details to stand alone. The full manuscript reports mean positional errors below 1 cm for the best-matching embodiment (iCub), computed via frame-by-frame comparison of retargeted joint positions to the source video pose estimates. In the revision we will update the abstract to reference these metrics explicitly and point to the Results section for distributions (to be added as box plots) and the measurement protocol (least-squares alignment of 3D keypoints after scaling). Baseline comparisons to naive retargeting without optimization will also be included. Direct validation against ground-truth infant motion-capture data remains unavailable; we instead benchmarked the pose estimator on adult video datasets with known 3D ground truth and on synthetic infant-like renders. This practical limitation will be stated clearly. revision: partial

  2. Referee: [Methods] Methods (retargeting pipeline): body scaling and joint-mapping parameters are treated as free parameters yet no procedure is given for selecting or validating them so that the resulting proprioceptive, tactile, and visual streams remain functionally equivalent to infant experience despite large differences in body proportions and sensor placement.

    Authors: We agree that the parameter selection process requires explicit description. Scaling factors and joint correspondences were determined by minimizing the sum of squared Euclidean distances between corresponding 3D keypoints after rigid alignment, using a gradient-free optimizer (Nelder-Mead) initialized from anthropometric tables. Validation consisted of both quantitative kinematic error and qualitative visual inspection of retargeted motions. We will insert a dedicated subsection in Methods detailing the objective function, initialization, and convergence criteria. We will also add a limitations paragraph noting that perfect functional equivalence is precluded by embodiment mismatches and that the pipeline prioritizes preservation of relative motion trajectories and resulting sensor activation patterns rather than absolute equivalence. revision: yes

  3. Referee: [Results] Results: no sensitivity analysis or quantitative comparison of derived quantities (joint-torque distributions, contact-force ranges, visual-field coverage) against published infant literature values is reported, so the claim that the streams 'enable a rich multimodal analysis' rests only on kinematic reproduction.

    Authors: This comment correctly identifies an opportunity to strengthen the multimodal claims. In the revised manuscript we will add a sensitivity analysis subsection that perturbs scaling and mapping parameters within plausible ranges and reports resulting variations in joint-torque histograms, contact-force magnitudes, and visual-field coverage metrics. We will further compare the obtained ranges against values drawn from the infant motor-control literature (e.g., peak forces during reaching and developmental changes in visual field extent). These additions will demonstrate that the generated streams produce sensorimotor statistics consistent with published infant data, thereby supporting the utility claim beyond kinematics alone. revision: yes

standing simulated objections not resolved
  • Direct validation of retargeted kinematics against ground-truth 3D motion-capture recordings from infants, which is currently infeasible owing to ethical, safety, and technical constraints on acquiring such data from young infants.

Circularity Check

0 steps flagged

No circularity: standard pose estimation and retargeting pipeline with no self-referential equations or load-bearing self-citations

full rationale

The paper presents a methodological pipeline: extract skeletal structure and 3D pose from infant video, then retarget the motion to iCub/pyCub/EMFANT/MIMo embodiments to generate proprioceptive, tactile, and visual streams. No equations, fitted parameters, or derivations are described that reduce the output sensor streams to inputs by construction. The abstract and provided text contain no self-citations that justify uniqueness or force the central claim. The reader's assessment of score 2.0 aligns with this; the skeptic concern is about unquantified experiential fidelity (a correctness/validity issue), not circularity in the derivation chain. The work is self-contained against external benchmarks of pose estimation and retargeting accuracy.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The method depends on standard computer-vision assumptions for 3D pose recovery and conventional retargeting mappings; no new physical entities are introduced and only routine scaling parameters are required.

free parameters (1)
  • body scaling and joint mapping parameters
    Required to adapt infant skeletal proportions and ranges to the target humanoid embodiments; values are chosen per platform.
axioms (1)
  • domain assumption Infant body configuration can be reliably recovered from monocular video via skeletal extraction and 3D pose estimation
    Invoked in the first reconstruction step; accuracy of this step is taken as given.

pith-pipeline@v0.9.0 · 5566 in / 1409 out tokens · 37242 ms · 2026-05-07T09:50:13.502974+00:00 · methodology

discussion (0)

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Reference graph

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