MotionPersona: Real-Time Locomotion Control across Personas, Bodies, and Styles

Mingyi Shi; Wei Liu; Jidong Mei; Wangpok Tse; Rui Chen; Xuelin Chen; Taku Komura

cs.GRMay 30, 2025

MotionPersona: Real-Time Locomotion Control across Personas, Bodies, and Styles

Authors: Mingyi Shi, Wei Liu, Jidong Mei, Wangpok Tse, Rui Chen, Xuelin Chen, Taku Komura

Organizations: The University of Hong Kong, Hong Kong · Shandong University, China · Hong Kong University of Science and Technology, Hong Kong · Adobe Research, UK · TransGP, Hong Kong

Abstract

We present MotionPersona, a generative framework for character-aware locomotion control, in which the motion for a command depends on the captured persona, the body shape, and the character's style. Unlike style, which one performer can vary at will, persona and body shape are coupled in capture: each performer is observed in only one body. The captured data therefore cannot uniquely determine which motion characteristics should follow the persona and which should change with the body, leaving unseen persona-body combinations unconstrained. We capture 48 performers, aged 5 to 68, under the same nine styles and seven commands, 44 of them with persona annotation. From this repeated-measures design, we identify two robust associations between body shape and gait. These measurements guide a cross-body specification of which characteristics should change and which should be preserved. We implement this specification through a physically informed retargeting pipeline, producing cross-body training data while penalizing penetration and foot skating. On this data we train a single generative controller. A shape-aware VAE compresses each motion block into a few latent tokens and renders them on a conditioned target body under explicit geometric supervision; over these tokens, a latent flow-matching prior generates persona- and style-conditioned motion in two sampling steps. The controller covers all captured personas, a wide family of SMPL-X target bodies, and nine styles in one model, and runs at 27 ms per block on two threads of a laptop CPU. We verify the framework at every stage, following the same gait descriptors from captured to retargeted to generated motion and sweeping each axis in isolation. To our knowledge, this is the first real-time locomotion controller that carries part of a captured persona's performer-specific variation across independently selected body shapes and styles.

Figures & tables

Figure 1 . We present a generative character-aware locomotion controller whose three controls specify a captured motion persona (who is moving), a target body (which body carries the motion), and a performed style (how the character is moving) under varying locomotion commands. Within the captured catalog and target-body family, one shared model recombines the three controls in real time. Code and data will be fully released.

Figure 2 . Why real capture binds persona to body. (a) Each captured performer brings one body, so capture fills only the diagonal of the persona

\times

body grid (schematic; 44 performers in our data); every other cell, such as one persona on another body, is never observed, and the cross design of Section 4 constructs it by retargeting. (b) Within one performer, an 8 ∘ forward lean splits into body, persona, and remainder as

B+P+u

in many ways; moving any

g

from

P

B

explains the clips equally well.

Figure 3 . Wider hips go with more forward and side trunk lean, the two associations assigned to shape. Grey points are the adult performers’ effects on trunk lean, the performer term of Equation 2 , against hip width; blue points are the means of the four hip-width quartiles with standard errors. The dashed line and the band are the fit over all 39 adults and its bootstrap 95% interval; the pair slope comes from the twelve metadata-matched adult pairs that share all three attributes but differ in body.

Figure 4 . Cross-body retargeting. One frame of an angry forward walk on the source body (left) and seven target bodies of increasing height and girth (right: a captured child and six grid bodies), shown with a fixed camera rig and SMPL-X body masses. Stance widens, arms clear the torso and thighs, and trunk lean follows the assigned hip-width associations. On the widest body the plain copy penetrates by up to 14 cm; the optimizer brings this to 1.6 cm, and the mean forward lean runs from $-6.7^{\circ}$ on the child body to $+3.5^{\circ}$ on the widest.

	Capture	Assigned	MotionBuilder	Ours	Sign
Forward lean, ∘	2.0	1.4	0.0	1.9	0.92
Side lean, ∘	0.4	0.6	0.0	0.6	0.79
Hand distance	0.23	–	0.00	0.03	0.54
Cadence (preservation target), Hz	0.23	–	0.00	0.02	0.54
Penetration, % of frames $\downarrow$	8.9	0	8.2	0.0	–
Lowest toe, cm	$-4.1$	$\geq 0$	$-4.0$	$\mathbf{-0.6}$	–

Table 1 . Ours moves the lean toward the real partner and removes floor penetration; MotionBuilder does neither. Upper rows: medians over 24 transfers within twelve pairs; Capture is the real difference between the two performers, Assigned the part the associations predict from their hip widths, and Sign the fraction of transfers in which ours moves toward the partner. Lower rows: 100 clips both retargeters produced on the same body.

Figure 5 . The controller: a shape-aware VAE realizes the motion on the target body, and a latent prior selects it. Dashed outlines mark the modules each stage trains. The shape-aware VAE decoder (a) realizes every block on the target body, continues the previous block, and is trained with explicit forward-kinematic and contact losses. The prior (b) generates the next latent tokens from the command, target shape, persona ID and typed attributes, and style, in two steps.

Condition	Form	Enters	Drop
History	last 5 frames	decoder	–
	1 token, codec encoder	prior	15%
Trajectory	45 positions, 45 facings	prior	–
Persona ID $s$	44-way learned embedding	prior	–
Attributes $\mathbf{a}_{s}$	three typed embeddings	prior	–
Style $y$	label embedding	prior	–

Table 2 . Condition design and training dropout. Only history is dropped, enabling guidance in Equation 13 .

Step	ms
Featurization of the history and the trajectory	0.3
History encoder, one token	1.1
Prior, two Euler steps	13.7
Decoder, per-frame queries and temporal head	5.6
Block, M3 Max CPU, two threads	26.5
Block, RTX 5080 GPU	9.9

Table 3 . Block runtime. Steps are timed separately on two threads of an Apple M3 Max CPU and include individual call overheads; totals are measured end-to-end, not summed.

		Quality							Control		Spread kept
	Test set	FPD pose $\downarrow$	FPD contact $\downarrow$	Jerk $\downarrow$	Skate $\downarrow$	IoU $\uparrow$	Pen. % $\downarrow$	Seam $\downarrow$	Rec. $\uparrow$	Style $\uparrow$	Dyn. $\uparrow$	Amp. $\uparrow$
MotionPersona-X (data)	own body	–	0.83	0.51	0.34	0.57	0.5	–	0.889	0.80	1.00	1.00
CAMDM ( Chen et al., 2024 )	own body	0.30	1.90	0.65	0.60	0.49	49.0	3.52	0.323	0.32	0.43	0.55
	cross-body, seen	0.28	3.28	0.51	0.53	0.50	21.8	3.10	0.257	0.51	$\cdot$	$\cdot$
	cross-body, withheld	0.27	3.25	0.51	0.53	0.51	21.8	3.10	0.255	0.42	$\cdot$	$\cdot$
	held-out bodies	0.28	3.42	0.51	0.53	0.50	23.9	3.11	0.256	0.40	$\cdot$	$\cdot$

Table 4 . Ours leads the baseline in control and in most quality columns on all four test sets, and its quality holds off the performer’s body. Own body: every persona in every style on its own body; cross-body: every persona in every style on training bodies it was paired with, on training bodies whose pairing was withheld, and on bodies training never saw. Jerk is the median third difference of the toe positions in cm per frame 3 (the data value is measured on the reference takes), skate and seam in cm per frame, penetration in percent of frames, recovery and style accuracy as retrieval rates, with the held-out takes retrieved against each other as the data reference; spread kept exists on the own-body set only. Own body + retargeter is the modular pipeline: our controller generates on the performer’s own body, and our retargeter carries the result to the target body. Bold marks the best method per test set.

Figure 6 . The baseline loses the floor at both ends of the height range; ours keeps it. One persona walks straight on three sweep bodies (thumbnails at a common scale); blue marks hovering feet, red the sole below the floor. Numbers are the median height of the lowest foot vertex above the floor over 19 s; within $\pm 1$ cm counts as on the floor.

Figure 7 . One control at a time, with the command fixed. (a) One persona on five bodies, ordered by hip width: the trunk (blue, against the grey vertical) leans back less as the hips widen. (b) Five personas on one body: the foot-contact strips, four seconds of the left and right foot, show five rhythms. (c) Five styles on one persona and body change the whole manner of moving.

Figure 8 . The single-axis test, scored against the data. Each column changes one control and holds the other two and the command. (a) Body: forward lean and step length against hip width for all 44 personas on the sixteen sweep bodies, in the held-out takes (black), their codec reconstruction (grey, dashed), and the controller’s rollouts (blue); lines are means, bands the 10th to 90th percentile, numbers the mean and spread of the per-persona slopes per metre. (b, c) Persona and style: per descriptor, the spread the controller produces across the 44 personas or the nine styles, as a fraction of the data’s spread on the same body with each command’s mean removed; (c) averages over the personas; median over bodies with a bootstrap interval, 1 reproduces the data. Dots name the factor that owns the descriptor in the grid; open rows are fixed by the command and should stay near 0; ticks in (b) are a prior trained without a persona input.

	Lean slope		Preserved slope		Other bodies
	fwd	side	cadence	step	Pen. % $\downarrow$	Pres. $\downarrow$	Rec. $\uparrow$
target	150	65	0	0	0	0
MotionPersona-X (data)	150	65	$-0.3$	$+0.1$	0.3	–	–
Codec reconstruction	155	55	$0.0$	$0.0$	0.5	–	–
Captured grid	22	$-8$	$-0.2$	$+0.1$	60.9	0.25	0.235
Geometric copies	$-77$	6	$+0.7$	$-0.2$	0.0	0.97	0.341

Table 5 . What each step of the cross design contributes. The same controller trained on the captured grid, on the geometric copies, on the optimizer’s output without the lean targets, and on MotionPersona-X, with the data and the codec’s reconstruction above. Slopes per metre of hip width on the sweep bodies, targets under the headers (over all 44 personas, ours gives 115 and 28); penetration and preservation on the same bodies, recovery on the held-out bodies. Bold marks the trained row nearest its target; the copies row does not touch the floor, so its penetration is not bolded. Pres. is the relative change of cadence and step length from the own-body reference.

Figure 9 . The baseline averages two performers’ pelvis bob; ours keeps part of the difference. Left: side-view strobes over 1.4 s, each performer on her own body at a common scale, with the pelvis trace exaggerated five times. Right: pelvis height minus its trend at true scale; labels give the peak-to-peak amplitude over 15 s.

	FPD $\downarrow$							ms
Backbone	pose	contact	Pen. % $\downarrow$	Skate $\downarrow$	IoU $\uparrow$	Style $\uparrow$	Spread $\uparrow$	GPU	CPU
Data	–	0.83	0.5	0.34	0.57	0.80	1.00	–	–
FM pose, 2 steps	1.74	288	2.2	–	0.00	0.52	–	6.2	44
DDPM pose, 4 steps	0.28	1.90	18.9	0.57	0.49	0.37	0.61	9.9	87
FM token, 2 steps	0.32	1.70	1.0	0.49	0.54	0.74	0.70	9.9	27

Table 6 . One transformer trained three ways on MotionPersona-X, own-body set: flow matching in pose space, four-step diffusion in pose space with CAMDM’s sampler, and two-step flow matching in the codec’s token space, the controller. Spread is the amplitude group of Table 4 ; ms per block on an RTX 5080 and on two CPU threads. Bold marks the best of the two rows that walk; the first does not walk under either commit schedule and is reported under the three-frame one.

Criterion	Ours	CAMDM
Realism of the motion $\uparrow$	3.97	3.22
Match to the persona $\uparrow$	4.17	2.59
Match to the style $\uparrow$	3.91	3.82
Body-shape artifacts $\downarrow$	1.26	2.17
Responsiveness $\uparrow$	3.71	3.31
Follows the command $\uparrow$	4.12	3.79

Table 7 . Practitioners rate ours higher on five criteria and level on style: mean of all ratings collected from 28 professionals on a five-point scale. Lower is better for artifacts; bold marks the better controller.

	Reconstruction		Rollout
Codec	IoU $\uparrow$	Lean $\to$ 1	Skate $\downarrow$	IoU $\uparrow$	Seam $\downarrow$	Pen. % $\downarrow$	Lean $\to$ 1
Reference	1.00	1.03	0.49	0.54	3.45	1.0	0.70
No $\boldsymbol{\beta}$ in the decoder	1.00	0.99	0.55	0.35	3.46	42.0	0.61
No boundary frames	1.00	1.02	0.62	0.53	3.81	0.9	1.17

Table 8 . Ablations on the codec, on MotionPersona-X: one decision changed and the prior retrained. Reconstruction columns are frame-aligned with the source, with the reference-label IoU; rollout columns follow the protocol on the own-body set. Lean is the forward-lean slope as a fraction of the data’s, after reconstruction and after rollout, with a target of 1; bold marks the best measured row, and for Lean the row nearest 1.

	FPD $\downarrow$
Prior	pose	contact	Skate $\downarrow$	IoU $\uparrow$	Track. $\downarrow$	Rec. $\uparrow$	Style $\uparrow$	Spread $\uparrow$	Lean $\to$ 1
Reference	0.32	1.70	0.49	0.54	5.5	0.632	0.74	0.70	0.70
Dropout 0	0.29	2.23	0.53	0.48	2.9	0.494	0.73	0.66	1.28
No shape token	0.33	1.90	0.65	0.48	7.8	0.629	0.73	0.76	0.61
No persona	0.48	2.24	0.52	0.51	5.9	0.318	0.53	0.19	1.00
ID only	0.32	1.70	0.49	0.54	5.4	0.644	0.66	0.69	0.82

Table 9 . Ablations on the prior, on MotionPersona-X, own-body set: one decision changed per row on the final codec. Track. is the root error in cm, Rec. persona recovery, Style the style accuracy, Spread the amplitude group, Lean the forward-lean slope as a fraction of the data’s on the sweep bodies, with a target of 1; bold marks the best measured row, and for Lean the row nearest 1. Rows within 0.03 of skate or 0.04 of recovery of the reference are within one training seed’s spread and are not ranked.

Figure 10 . In-betweening on a 105 cm body: MotionBricks ( Wang et al., 2026 ) , which has no body input, sinks the feet into the floor for the whole gap, whereas ours keeps them where the data has them in this example. Both receive the same keyframes (gold) and root path of a backward walk and generate the 40 frames between them. (a) Poses spread horizontally for legibility, with the feet shown at frame 5. (b) Lowest toe joint height: minimum $+1.8$ cm in the data, $+1.9$ cm for ours, and $-2.1$ cm for MotionBricks.

Figure 11 . Speech-driven gesture: one speech clip of a BEAT2 speaker drives five other speakers on their own bodies, short to tall, each in its own manner. Left, the speaker’s motion capture; feet locked after generation.

Appendix figures & tables20 assets

Supplementary material from the paper’s appendix.

Appendix

	Performer profile and source body								Style		Command
Take name	Age	Gender	Height (cm)	Weight (kg)	Beta	Role	Affiliation	Dominance	Label	Group	Base movement
s001_angry_fw	5	M	112	17	[-5.68, 1.26, …]	Child	Reserved	Compliant	Angry	Affective	forward walking
s015_neutral_bw	22	F	165	47	[0.96, 0.27, …]	Student	Sociable	Assertive	Neutral	Affective	backward walking
s032_twofootjump_fr	23	M	189	80	[1.44, -1.35, …]	Athlete	Gregarious	Dominant	Two-foot jump	Locomotion variant	forward running

Appendix

Table 10 . Example annotations. Each clip is linked to a closed-set performer ID, three typed persona attributes, the performer’s source-body metadata, a style label, and a locomotion command. Beta is the 10-dimensional SMPL-X shape vector; age and gender describe the capture cohort but are not persona conditions.

		Locomotion coverage					Performers				Scale and annotation
Dataset	Accessible	Forward	Backward	Sideways	Fingers	#Perf.	Age	Height	Weight	Text	#Seq	Hours	SMPL
Edinburgh ( 2017 )	✓	✓	✓	✓	✗	–	✗	✗	✗	✗	80	1	✗
LAFAN1 ( 2020 )	✓	✓	✓	✓	✗	5	✗	✗	✗	✗	77	4.6	✗
BFA ( 2020b )	✓	✓	✗	✗	✗	1	✗	✗	✗	✗	33	1.5	✗
100STYLE ( 2022 )	✓	✓	✓	✓	✗	1	✗	✗	✗	✗	810	18.75	✗
Multi-sub ( 2024 )	✗	✓	✓	✗	✗	12	✗	154–195	✗	✗	–	4	✗

Appendix

Table 11 . Comparison with locomotion datasets. MotionPersona combines performer diversity, repeated styles, repeated commands, and SMPL-X bodies. “Text” denotes performer- or style-level annotation rather than motion-content captions.

Figure 12 . Top: eight of the SMPL-X bodies fitted to the performers. Bottom: the distribution of height, weight, and age. Blue points are male performers, orange points female.

Performer	Ang	Big	Dep	Dru	Fea	Hap	Neu	Swi	Jmp	Sum	Performer	Ang	Big	Dep	Dru	Fea	Hap	Neu	Swi	Jmp	Sum
P01	6	6	6	7	6	6	6	6	6	55	P23	6	6	6	6	6	6	6	6	0	48
P02	7	7	7	7	7	7	7	7	7	63	P24	7	7	7	7	7	7	7	7	7	63
P03	6	4	5	3	3	1	7	1	5	35	P25	7	7	7	7	7	7	7	7	7	63
P04	7	7	7	7	7	7	6	7	7	62	P26	7	7	7	7	7	7	6	7	7	62
P05	7	7	7	7	7	7	7	7	7	63	P27	7	7	7	7	7	7	7	7	7	63
P06	7	7	7	7	7	7	7	7	7	63	P28	7	7	7	7	7	7	7	7	7	63

Appendix

Table 12 . Clips per performer and style in the analysed grid; each style holds at most seven clips, one per command. Sixteen performers fill all 63 cells, and P33, P43, P37, P03, and P29 miss many. Columns are angry, big step, depressed, drunk, fear, happy, neutral, swimming, and two-foot jump.

Group	Descriptor	Definition and estimator	Unit
Timing	Cadence	Steps per second, $2/T$ , where the stride period $T$ is the first autocorrelation peak of each ankle’s height; the median over 10-second sliding windows	steps/s
	Cadence (events) †	Number of contact onsets of both feet divided by the duration; kept only as a check, since it undercounts steps	steps/s
	Contact ratio	Fraction of frames in which a foot is in contact; contact starts when the ankle or toe is lower than $\max(3\%\ \text{leg},2\,\text{cm})$ and ends when both are higher than $\max(6\%\ \text{leg},4\,\text{cm})$	fraction
	Contact ratio (labels) †	The same from the stored contact labels (foot speed below a threshold and height at most 3 cm), which miss most stance frames in running	fraction
	Double support	Fraction of frames with both feet in contact	fraction
	Step regularity	Coefficient of variation of the step intervals (higher is less regular)	–

Appendix

Table 13 . The 27 gait descriptors, computed in the pelvis-facing frame from forward-kinematics joint positions. Lengths are divided by the performer’s leg length, hip width, or half shoulder width, so similarity scaling leaves them unchanged. Daggers mark the four reference quantities, which enter only the variance decomposition and the first body fits, as checks.

Descriptor	Style	Command	Performer	Interaction	Residual	Noise	Reliability
Speed	0.02	0.21	0.52	0.10	0.16	0.09	0.86–0.93
Jerk	0.03	0.13	0.51	0.20	0.14	0.02	0.76–0.96
Sway	0.02	0.21	0.47	0.11	0.20	0.09	0.86–0.87
Contact ratio	0.10	0.28	0.33	0.09	0.20	0.09	0.87–0.92
Forward lean	0.25	0.02	0.25	0.36	0.13	0.04	0.73–0.94
Cadence	0.04	0.16	0.21	0.18	0.42	0.02	0.73–0.74

Appendix

Table 14 . Variance decomposition on the grid, as fractions of total variance under the additive model in the main paper. Residual is what the four terms leave; for cadence most of it is estimator variance. Noise is the robust within-clip floor, from the median absolute deviation over 10-second segments. Reliability is the split-half correlation of the performer effect, across style groups and across walking versus running.

	Order A: style first					Order B: performer first			Noise floor		Split-half reliability
Descriptor	Style	Command	Performer	Interaction	Residual	Performer	Command	Style	Raw	Robust	Styles	Walk/run
Cadence	0.04	0.16	0.21	0.18	0.42	0.21	0.16	0.04	0.65	0.02	0.73	0.74
Cadence (events) †	0.04	0.27	0.30	0.12	0.27	0.30	0.27	0.04	0.22	0.11	0.81	0.88
Speed	0.02	0.21	0.52	0.10	0.16	0.52	0.20	0.03	0.24	0.09	0.86	0.93
Step length	0.16	0.15	0.27	0.12	0.30	0.27	0.14	0.17	0.54	0.12	0.90	0.87
Contact ratio	0.10	0.28	0.33	0.09	0.20	0.33	0.28	0.09	0.24	0.09	0.92	0.87

Appendix

Table 15 . Variance decomposition of all 27 descriptors, as fractions of the total sum of squares. The performer share exceeds the robust noise floor for every descriptor except step regularity and head sway, and changes by at most 0.01 when the performer term is fitted first. Interaction and residual are identical in both orders and listed once; the robust noise floor uses a MAD-based variance, because about 3% of the 10-second cadence and step-length estimates are octave errors.

Descriptor	Full	Compact	Full	Compact	All	Adults	Strongest adult correlate ( $r$ )	All	Adults	Body residual
	Body, all 44		Body, 39 adults		Body, nested			Attributes
Cadence	$-$ 0.03	$-$ 0.17	$-$ 0.00	$-$ 0.08	$-$ 0.12	$-$ 0.03	arm/leg ( $-$ 0.20)	$-$ 0.01	$-$ 0.02	$-$ 0.12
Cadence (events) †	$-$ 0.08	$-$ 0.11	$-$ 0.04	$-$ 0.02	–	–	BMI ( $-$ 0.32)	0.08	0.09	0.07
Speed	$-$ 0.30	$-$ 0.23	0.06	$-$ 0.04	$-$ 0.13	$-$ 0.07	arm/leg ( $-$ 0.25)	0.02	0.07	$-$ 0.06
Step length	$-$ 0.29	$-$ 0.27	0.24	0.20	$-$ 0.32	0.22	BMI ( $-$ 0.33)	0.00	0.09	$-$ 0.11
Contact ratio	$-$ 0.07	$-$ 0.07	$-$ 0.12	$-$ 0.08	$-$ 0.16	0.07	arm/leg ( $-$ 0.33)	$-$ 0.22	$-$ 0.23	$-$ 0.21

Appendix

Table 16 . Body measures and attributes predict little of the performer effect. Values are leave-one-performer-out

R^{2}

, negative when worse than the mean; full uses the nine body measures, compact leg length, BMI, child, and female, and nested selects one or two measures inside each training fold (reference quantities not fitted). Attributes are the role one-hot with ordinal affiliation and dominance; the last column fits them to the residual of the better body model.

		Leave-one-out $R^{2}$				Correlation of the pair
Descriptor	Body pair	Pair	Age	Pair + age	Pair, under 53	Raw $r$	$r$ after age
Speed	height, BMI	0.07	0.30	0.27	$-$ 0.10	$+$ 0.24, $-$ 0.21	$+$ 0.05, $+$ 0.02
Step length	height, BMI	0.26	0.37	0.38	$-$ 0.07	$+$ 0.32, $-$ 0.33	$+$ 0.14, $-$ 0.12
Foot clearance	height, weight	0.16	0.20	0.20	$-$ 0.03	$+$ 0.21, $-$ 0.14	$+$ 0.05, $-$ 0.12
Pelvis bob	height, arm/leg	0.13	0.23	0.29	0.02	$+$ 0.29, $-$ 0.50	$+$ 0.12, $-$ 0.43
Pelvis sway	height, BMI	0.06	0.19	0.15	$-$ 0.09	$+$ 0.24, $-$ 0.19	$+$ 0.08, $+$ 0.00

Appendix

Table 17 . Age control and partial correlation for the 15 descriptors whose best adult pair of body measures has a positive leave-one-out

R^{2}

. Refitting the pair on the 31 adults younger than 53 leaves an

R^{2}

of at most 0.02 for speed, step length, foot clearance, pelvis bob and sway, knee range, and joint speed, while forward and side lean keep their hip-width correlation after partialling out age. The pair is chosen on the same 39 adults, so its

R^{2}

is optimistic.

Role / affiliation / dominance	Performers	Pairs
Athlete/sociable/assertive	P12 (189/0.88), P22 (180/0.85), P38 (168/0.74)	3
Professional/moderate/dominant	P34 (160/0.79), P39 (160/0.72)	1
Retiree/moderate/compliant	P01 (159/0.74), P33 (173/0.73)	1
Student/gregarious/assertive	P15 (182/0.85), P32 (175/0.75)	1
Student/reserved/moderate	P28 (160/0.69), P29 (163/0.73), P36 (171/0.80)	3
Student/withdrawn/submissive	P03 (165/0.73), P16 (179/0.80), P31 (164/0.72)	3

Appendix

Table 18 . The twelve metadata-matched adult pairs come from six attribute cells shared by two or three adults of different body. Each performer is listed with height (cm) and leg length (m); the two child cells, with two more pairs, are left out.

Descriptor	Body measure	Pairs	Adult slope	Paired slope	95% interval	Agree	$\rho$	$p$
Forward lean	hip w.	12	$+$ 226	$+$ 131	[ $+$ 41.3, $+$ 249]	0.67	0.71	0.01
Forward lean	BMI	12	$+$ 0.48	$+$ 0.177	[ $-$ 0.0592, $+$ 0.612]	0.58	0.59	0.04
Forward lean	weight	12	$+$ 0.0966	$+$ 0.0721	[ $+$ 0.00871, $+$ 0.193]	0.67	0.72	0.01
Step length	BMI	12	$-$ 0.00855	$+$ 0.00161	[ $-$ 0.00598, $+$ 0.00883]	0.50	$-$ 0.06	0.85
Step length	weight	12	$-$ 0.000453	$+$ 0.000497	[ $-$ 0.00114, $+$ 0.002]	0.58	0.13	0.68
Knee range	BMI	12	$-$ 0.679	$-$ 0.117	[ $-$ 1.52, $+$ 0.686]	0.50	$-$ 0.29	0.35

Appendix

Table 19 . Within the metadata-matched adult pairs, only forward lean (on hip width, BMI, and weight) and side lean (on hip width) keep a rank correlation with

p<0.05

. The adult slope is fitted across the 39 adults, the paired slope through the origin on the within-pair differences with a bootstrap 95% interval, in descriptor units per unit of the body measure (hip width and leg length in metres, height in cm, weight in kg). Agree is the fraction of pairs whose difference has the sign of the adult slope;

\rho

is the Spearman correlation of the pair differences.

Figure 13 . The 44 annotated performers grouped into their 33 role, affiliation, and dominance cells and plotted at their body heights. Twenty-five cells hold one performer; the other eight hold two or three, 14 pairs in all, which provide the metadata-matched identities of the real-pair check, not one persona in several bodies.

	Age, 39 adults				53 and over		$R^{2}$
Descriptor	$r$	$R^{2}$	$p$	+20 years	$d$	$p$	Flag	Under 53
Cadence	$-$ 0.20	$-$ 0.07	0.34	$-$ 0.102	$-$ 0.41	0.27	$-$ 0.07	$-$ 0.08
Speed	$-$ 0.61	0.30	0.00	$-$ 0.192	$-$ 1.54	0.00	0.23	$-$ 0.00
Step length	$-$ 0.65	0.37	0.00	$-$ 0.0687	$-$ 1.78	0.00	0.30	$-$ 0.03
Contact ratio	0.22	$-$ 0.10	0.62	$+$ 0.0235	0.57	0.46	–	–
Double support	0.35	0.00	0.05	$+$ 0.0576	0.98	0.13	–	–

Appendix

Table 20 . Age lowers speed, step length, foot clearance, pelvis bob and sway, knee and hip range, head bob, and joint speed among the adults, and the effect is carried by the eight performers aged 53 and over. Effects per +20 years are in the units of Table 13 ;

d

and the Welch

p

compare the eight with the 31 younger adults. The last two columns are the leave-one-out

R^{2}

of the age-53 flag and of age within the younger adults alone.

	Difference	% of mean	$d$	105 cm, 6 y	110 cm, 5 y	112 cm, 5 y	118 cm, 8 y	Small	142 cm, 9 y	P26 / small
				P43	P37	P13	P04		P26
Cadence	$+$ 0.548	$+$ 22%	1.15	$+$ 0.3	$+$ 0.5	$-$ 0.3	$+$ 1.1	0.42	$+$ 5.1	12.24
Speed	$+$ 0.248	$+$ 25%	0.90	$+$ 2.2	$+$ 2.5	$+$ 0.7	$+$ 1.2	1.48	$-$ 1.8	$-$ 1.20
Step length	$+$ 0.0743	$+$ 17%	0.77	$+$ 2.3	$+$ 3.0	$+$ 0.9	$+$ 0.6	1.48	$-$ 2.3	$-$ 1.59
Contact ratio	$-$ 0.0308	$-$ 4%	$-$ 0.36	$+$ 0.4	$-$ 1.8	$-$ 0.8	$+$ 0.1	–	$+$ 0.3	–
Double support	$-$ 0.0303	$-$ 5%	$-$ 0.24	$+$ 0.4	$-$ 1.2	$-$ 0.8	$+$ 0.1	–	$+$ 0.4	–

Appendix

Table 21 . The children differ from the adults most in foot clearance and step width, but the tallest child is not an interpolation between the small children and the adults. Difference is children minus adults on the performer effect in the units of Table 13 , also as a percentage of the grand mean; per-child values are

z

-scores against the adults, with P43’s from two clips. Small is the mean

z

of P37, P13, and P04, and the last column is P26’s

z

as a fraction of it.

	Forward lean		Side lean
Subset	Slope [95% interval]	$R^{2}$	Slope [95% interval]	$R^{2}$
Angry	$+$ 3.06 [ $+$ 0.18, $+$ 6.21]	$+$ 0.00	$+$ 0.43 [ $-$ 0.05, $+$ 1.12]	$-$ 0.06
Big step	$+$ 5.11 [ $+$ 1.80, $+$ 9.34]	$+$ 0.10	$+$ 0.77 [ $+$ 0.29, $+$ 1.27]	$+$ 0.07
Depressed	$+$ 9.18 [ $+$ 3.69, $+$ 15.29]	$+$ 0.10	$+$ 1.21 [ $+$ 0.43, $+$ 1.99]	$+$ 0.08
Drunk	$+$ 6.51 [ $+$ 1.05, $+$ 13.45]	$+$ 0.04	$+$ 1.57 [ $+$ 0.54, $+$ 2.59]	$+$ 0.14
Fear	$+$ 10.75 [ $+$ 4.90, $+$ 18.97]	$+$ 0.12	$+$ 1.01 [ $+$ 0.18, $+$ 2.18]	$-$ 0.01

Appendix

Table 22 . The slopes of forward and side lean on hip width are positive in every style and in walking, running, and transitions, although several single-style intervals include zero. Slopes are degrees per +2 cm of hip width, fitted on the adults’ per-performer means, with bootstrap 95% intervals and leave-one-out

R^{2}

$n$	$p$	True $R^{2}$	Median	5%	95%	$P(>0.15)$	$P(>0)$
39	1	0.00	$-$ 0.09	$-$ 0.14	0.02	0.00	0.07
39	1	0.05	$-$ 0.04	$-$ 0.12	0.14	0.05	0.32
39	1	0.10	0.01	$-$ 0.11	0.20	0.11	0.54
39	1	0.15	0.06	$-$ 0.10	0.28	0.24	0.68
39	1	0.20	0.10	$-$ 0.06	0.31	0.40	0.84
39	1	0.30	0.22	0.02	0.44	0.74	0.96

Appendix

Table 23 . At 39 or 44 performers, leave-one-out

R^{2}

falls well below the true

R^{2}

, and a true effect of 0.15 exceeds 0.15 in at most a quarter of the simulations. Features are Gaussian with one true linear effect;

p=1

is univariate least squares,

p=4

ridge on four features of which one is real. The columns give the median and the 5th and 95th percentiles of the leave-one-out

R^{2}

, and the probability that it exceeds 0.15 and 0.

			39 adults		All 44
Descriptor	Share	Reliability	Shape	+ age	Shape	+ age	Child
Cadence	0.21	0.84	0.00	0.00	0.00	0.00	0.00
Speed	0.52	0.92	0.00	0.30	0.01	0.29	0.00
Step length	0.27	0.93	0.22	0.37	0.03	0.28	0.00
Contact ratio	0.33	0.93	0.07	0.07	0.04	0.04	0.00
Double support	0.28	0.93	0.00	0.00	0.03	0.03	0.00

Appendix

Table 24 . A shape predictor fitted on the adults explains 7.5% of the performer differences and 2.0% of the total variance; allowing age nearly doubles both. Share is the performer share

s_{d}

, and reliability the Spearman–Brown split-half reliability that bounds any predictor. The adult columns use shape alone or shape and age; the columns for all 44 use shape with the child flag, shape and age, or the child flag alone.

Term	What it asks	Depends on	$w$
Fidelity (rotation, root)	stay close to the kinematic copy	–	1, 0.01
Penetration	penalize block overlap beyond an allowance	block distance fields	1
Ground	penalize sole corners below the floor	foot geometry	1
Stance	penalize contact height away from the floor	foot geometry	1
Slide	penalize corner velocity during contact	foot geometry	5
Anchor	penalize drift from the copy’s contact-run anchors	foot geometry	10

Appendix

Table 25 . Terms of the retargeting objective, what in the body each depends on, and the weights of the production run.

Figure 14 . The evaluation trajectory, colored by commanded speed; every test case follows this one-minute control sequence, scaled with leg length.

	Shape-aware VAE	Latent FM prior	Raw-space baseline
Input	45 frames	9 tokens $\times$ 48	55 frames
Token span	5 frames	–	–
Decoder read-out	5 frame queries per token; temporal head $3\times$ depthwise conv, kernel 5, residual	–	–
Layers $\times$ width	6 + 6 $\times$ 384	8 $\times$ 384 (DiT)	8 $\times$ 256
Feed-forward, heads	1024, 4	1024, 4	1024, 4
Dropout	0.1	0.2	0.2

Appendix

Table 26 . Architecture and training of the two stages and of the raw-space baseline. Every epoch is about 6.1 M windows: 6,000 batches of 1,024 split over the GPUs for the two stages, and 2,667 steps of 2,304 windows for the baseline, whose per-GPU batch of 384 with two accumulation steps fits the memory of one card. The baseline pins its history prefix at every denoising step and therefore trains without history dropout.

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Apr 28, 2026, cs.CVJ/K move · Enter open · S save

IAM: Identity-Aware Human Motion and Shape Joint Generation

Wenqi Jia, Zekun Li, Abhay Mittal, Chengcheng Tang+5

UIUC · Meta Reality Labs · Brown University

Human Motion Generation Human Motion 3D Motion

Open paper AskPDF arXiv

Recent advances in text-driven human motion generation enable models to synthesize realistic motion sequences from natural language descriptions. However, most existing approaches assume identity-neutral motion and generate movements using a canonical body representation, ignoring the strong influence of body morphology on motion dynamics. In practice, attributes such as body proportions, mass distribution, and age significantly affect how actions are performed, and neglecting this coupling often leads to physically inconsistent motions. We propose an identity-aware motion generation framework that explicitly models the relationship between body morphology and motion dynamics. Instead of relying on explicit geometric measurements, identity is represented using multimodal signals, including natural language descriptions and visual cues. We further introduce a joint motion-shape generation paradigm that simultaneously synthesizes motion sequences and body shape parameters, allowing identity cues to directly modulate motion dynamics. Extensive experiments on motion capture datasets and large-scale in-the-wild videos demonstrate improved motion realism and motion-identity consistency while maintaining high motion quality. Project page: https://vjwq.github.io/IAM

Performer	Ang	Big	Dep	Dru	Fea	Hap	Neu	Swi	Jmp	Sum	Performer	Ang	Big	Dep	Dru	Fea	Hap	Neu	Swi	Jmp	Sum
P01	6	6	6	7	6	6	6	6	6	55	P23	6	6	6	6	6	6	6	6	0	48
P02	7	7	7	7	7	7	7	7	7	63	P24	7	7	7	7	7	7	7	7	7	63
P03	6	4	5	3	3	1	7	1	5	35	P25	7	7	7	7	7	7	7	7	7	63
P04	7	7	7	7	7	7	6	7	7	62	P26	7	7	7	7	7	7	6	7	7	62
P05	7	7	7	7	7	7	7	7	7	63	P27	7	7	7	7	7	7	7	7	7	63
P06	7	7	7	7	7	7	7	7	7	63	P28	7	7	7	7	7	7	7	7	7	63

Performer	Ang	Big	Dep	Dru	Fea	Hap	Neu	Swi	Jmp	Sum	Performer	Ang	Big	Dep	Dru	Fea	Hap	Neu	Swi	Jmp	Sum
P01	6	6	6	7	6	6	6	6	6	55	P23	6	6	6	6	6	6	6	6	0	48
P02	7	7	7	7	7	7	7	7	7	63	P24	7	7	7	7	7	7	7	7	7	63
P03	6	4	5	3	3	1	7	1	5	35	P25	7	7	7	7	7	7	7	7	7	63
P04	7	7	7	7	7	7	6	7	7	62	P26	7	7	7	7	7	7	6	7	7	62
P05	7	7	7	7	7	7	7	7	7	63	P27	7	7	7	7	7	7	7	7	7	63
P06	7	7	7	7	7	7	7	7	7	63	P28	7	7	7	7	7	7	7	7	7	63

Abstract

Explore similar work

IAM: Identity-Aware Human Motion and Shape Joint Generation

EMoG: Emotion-Modulated Gait Generation for Expressive Humanoid Locomotion

GPC: Large-Scale Generative Pretraining for Transferable Motor Control

Performer	Ang	Big	Dep	Dru	Fea	Hap	Neu	Swi	Jmp	Sum	Performer	Ang	Big	Dep	Dru	Fea	Hap	Neu	Swi	Jmp	Sum
P01	6	6	6	7	6	6	6	6	6	55	P23	6	6	6	6	6	6	6	6	0	48
P02	7	7	7	7	7	7	7	7	7	63	P24	7	7	7	7	7	7	7	7	7	63
P03	6	4	5	3	3	1	7	1	5	35	P25	7	7	7	7	7	7	7	7	7	63
P04	7	7	7	7	7	7	6	7	7	62	P26	7	7	7	7	7	7	6	7	7	62
P05	7	7	7	7	7	7	7	7	7	63	P27	7	7	7	7	7	7	7	7	7	63
P06	7	7	7	7	7	7	7	7	7	63	P28	7	7	7	7	7	7	7	7	7	63