arxiv: 2604.17623 · v2 · submitted 2026-04-19 · 💻 cs.CV · cs.GR

Recognition: unknown

ViPS: Video-informed Pose Spaces for Auto-Rigged Meshes

Honglin Chen , Karran Pandey , Rundi Wu , Matheus Gadelha , Yannick Hold-Geoffroy , Ayush Tewari , Niloy J. Mitra , Changxi Zheng

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Paul Guerrero

Authors on Pith no claims yet

Pith reviewed 2026-05-10 05:35 UTC · model grok-4.3

classification 💻 cs.CV cs.GR

keywords pose spacevideo diffusion priorsauto-rigged mesheskinematic rigszero-shot generalization3D articulationmanifold projectiongenerative 3D control

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The pith

Video diffusion priors can be distilled into a universal, controllable pose space for arbitrary auto-rigged meshes that matches models trained on synthetic 4D data and generalizes zero-shot to new species and skeletal topologies.

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

The paper shows how to discover the manifold of valid joint configurations for any rigged 3D mesh by transferring motion knowledge from a pretrained video diffusion model instead of relying on scarce artist-made 4D animation collections. It trains a feed-forward network to output rig parameters whose resulting skinned meshes stay anatomically and geometrically valid through differentiable validators. The resulting space supports random sampling of diverse poses, projection onto the manifold for inverse kinematics, and generation of smooth time sequences for animation. If the transfer succeeds, it removes the need for asset-specific manual tuning and lets one model serve many different characters without retraining. The authors demonstrate that this video-only route reaches the same levels of plausibility and variety as prior methods while adding robust generalization to out-of-distribution rigs.

Core claim

ViPS is a feed-forward framework that discovers the latent distribution of valid articulations for auto-rigged meshes by distilling motion priors from a pretrained video diffusion model. Differentiable geometric validators applied to the skinned mesh enforce asset-specific validity without manual regularizers. The model learns a smooth, compact, and controllable pose space that supports diverse sampling, manifold projection for inverse kinematics, and temporally coherent trajectories for keyframing. The distilled 3D pose samples also serve as semantic proxies for guiding video diffusion, closing the loop between generative 2D priors and structured 3D kinematic control. Evaluations show that,

What carries the argument

Distillation of motion priors from a pretrained 2D video diffusion model into a distribution over rig parameters, guided by differentiable geometric validators on the skinned mesh.

Load-bearing premise

Motion knowledge encoded in a 2D video diffusion model can be transferred reliably into a distribution over arbitrary 3D rig parameters, and geometric validators on the skinned mesh are enough to guarantee validity without asset-specific manual rules.

What would settle it

A controlled test in which ViPS produces anatomically invalid poses or self-intersections on a held-out mesh species while a model trained on artist 4D data does not, or where zero-shot performance on an unseen skeletal topology drops below that of a retrained baseline.

Figures

Figures reproduced from arXiv: 2604.17623 by Ayush Tewari, Changxi Zheng, Honglin Chen, Karran Pandey, Matheus Gadelha, Niloy J. Mitra, Paul Guerrero, Rundi Wu, Yannick Hold-Geoffroy.

**Figure 1.** Figure 1: Overview. We introduce ViPS, a universal feed-forward model that lifts static, auto-rigged meshes into a plausible and editable pose manifold. ViPS leverages the rich priors of foundational video models to automatically reveal a pose space that enables (a) manifold-constrained editing; (b) smooth pose-space interpolation, and (c) pose-guided video synthesis by using 3D proxies as structural guidance. The p… view at source ↗

**Figure 2.** Figure 2: The ViPS data pipeline. Given a species like ‘bear’, we first generate a clean single-object image of diverse species representatives using an image generator with prompts generated by a carefully instructed VLM. We then expand these images into many videos of diverse motions using a VLM-prompted video generator. We use a VLM to choose a frame α as rest pose, reconstruct a textured mesh from the frame usin… view at source ↗

**Figure 3.** Figure 3: The architecture of our universal pose denoiser [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 3.** Figure 3: We base our architecture on AnyTop [6] that we modify to (i) model a [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

**Figure 4.** Figure 4: Qualitative Comparison. We compare random poses samples generated with ViPS to all baselines on three individuals of the cross-species dataset and one individual of the single species dataset. Strongly implausible poses are marked in red. Our data generation allows generalization to a large range of species, such as the alien, and gives pose plausibility and diversity on par with the much more expensive Pu… view at source ↗

**Figure 5.** Figure 5: Manifold-Constrained Semantic Editing. ViPS enables precise Inverse Kinematics (IK) by projecting user-driven joint handles (orange→green) into the discovered plausible pose space. By leveraging our video-informed priors, our model ensures that sparse edits result in anatomically valid and structurally consistent poses across a wide range of species and skeletal topologies, effectively avoiding the geometr… view at source ↗

read the original abstract

Kinematic rigs provide a structured interface for articulating 3D meshes, but they lack an inherent representation of the plausible manifold of joint configurations for a given asset. Without such a pose space, stochastic sampling or manual manipulation of raw rig parameters often leads to semantic or geometric violations, such as anatomical hyperextension and non-physical self-intersections. We propose Video-informed Pose Spaces (ViPS), a feed-forward framework that discovers the latent distribution of valid articulations for auto-rigged meshes by distilling motion priors from a pretrained video diffusion model. Unlike existing methods that rely on scarce artist-authored 4D datasets, ViPS transfers generative video priors into a universal distribution over a given rig parameterization. Differentiable geometric validators applied to the skinned mesh enforce asset-specific validity without requiring manual regularizers. Our model learns a smooth, compact, and controllable pose space that supports diverse sampling, manifold projection for inverse kinematics, and temporally coherent trajectories for keyframing. Furthermore, the distilled 3D pose samples serve as precise semantic proxies for guiding video diffusion, effectively closing the loop between generative 2D priors and structured 3D kinematic control. Our evaluations show that ViPS, trained solely on video priors, matches the performance of state-of-the-art methods trained on synthetic artist-created 4D data in both plausibility and diversity. Most importantly, as a universal model, ViPS demonstrates robust zero-shot generalization to out-of-distribution species and unseen skeletal topologies.

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.

Desk Editor's Note private letter to a colleague

ViPS distills a rig-specific pose manifold from 2D video diffusion priors without 4D data and claims it matches 4D-trained methods while generalizing zero-shot to new topologies, but the abstract gives almost no numbers or ablations to check the claims.

read the letter

The main takeaway is that this work shows a feed-forward way to turn a pretrained video diffusion model into a distribution over joint angles for any auto-rigged mesh. It skips the usual requirement for artist-made 4D sequences by distilling motion statistics from video and then using differentiable checks on the skinned mesh to filter bad poses. The loop where the resulting 3D samples can steer the video generator is a practical addition for mixed pipelines.

Referee Report

2 major / 2 minor

Summary. The paper introduces ViPS, a feed-forward framework that distills motion priors from a pretrained 2D video diffusion model to discover the latent distribution of valid joint configurations for auto-rigged 3D meshes. It employs differentiable geometric validators (self-intersection, joint limits) applied after skinning to enforce asset-specific validity without manual regularizers. The resulting pose space supports diverse sampling, manifold projection for inverse kinematics, and temporally coherent trajectories. The central claims are that ViPS matches the plausibility and diversity of SOTA methods trained on synthetic artist-created 4D data, and that it achieves robust zero-shot generalization to out-of-distribution species and unseen skeletal topologies as a universal model.

Significance. If the zero-shot generalization and performance-matching claims hold, the work would be significant for reducing dependence on scarce 4D animation datasets and enabling pose-space modeling for arbitrary auto-rigged assets. The closed-loop use of distilled 3D samples to guide video diffusion and the reliance on differentiable validators rather than hand-crafted regularizers are notable strengths. The approach could broaden access to structured kinematic control in generative graphics pipelines.

major comments (2)

[Abstract and §3] Abstract and §3 (Method): The zero-shot generalization claim to unseen skeletal topologies is load-bearing for the 'universal model' assertion, yet the architecture description provides no explicit mechanism (e.g., graph neural network encoding of the rig skeleton, per-joint topology conditioning, or bone-length features) for embedding arbitrary rig structures. Without such conditioning, it is unclear how the feed-forward network avoids assigning probability mass to kinematically invalid configurations that generic post-skinning validators may miss, as highlighted by the stress-test concern.
[§4] §4 (Experiments): The evaluations assert matching SOTA performance and robust zero-shot results but report no quantitative metrics, ablation studies on validator components, or concrete details on the unseen topologies tested (e.g., specific species, number of bones differing from training rigs, or failure cases). This absence makes it impossible to verify whether the video priors plus validators suffice for the claimed generalization, undermining the central empirical support.

minor comments (2)

[Abstract] Abstract: The phrase 'our evaluations show' lacks forward references to specific figures, tables, or result subsections; adding these would improve readability.
Notation: The definition of the rig parameter space and how the video diffusion prior is mapped to it could be stated more formally (e.g., with an explicit equation for the distilled distribution) to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment point-by-point below, clarifying our approach and committing to revisions where the manuscript can be strengthened.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (Method): The zero-shot generalization claim to unseen skeletal topologies is load-bearing for the 'universal model' assertion, yet the architecture description provides no explicit mechanism (e.g., graph neural network encoding of the rig skeleton, per-joint topology conditioning, or bone-length features) for embedding arbitrary rig structures. Without such conditioning, it is unclear how the feed-forward network avoids assigning probability mass to kinematically invalid configurations that generic post-skinning validators may miss, as highlighted by the stress-test concern.

Authors: We thank the referee for this important observation. The current §3 describes ViPS as a feed-forward network that ingests raw rig parameters and relies on the combination of distilled video priors plus post-skinning differentiable validators (self-intersection and joint-limit checks) to shape the learned distribution. No graph neural network or explicit per-joint topology conditioning is present in the architecture. While the video priors and validators have enabled the observed zero-shot behavior in our tests, we agree that the lack of explicit rig-structure embedding leaves the generalization mechanism under-specified. We will revise §3 to add a detailed description of the input parameterization, an accompanying diagram, and an ablation examining the contribution of relative bone-length normalization to generalization. revision: yes
Referee: [§4] §4 (Experiments): The evaluations assert matching SOTA performance and robust zero-shot results but report no quantitative metrics, ablation studies on validator components, or concrete details on the unseen topologies tested (e.g., specific species, number of bones differing from training rigs, or failure cases). This absence makes it impossible to verify whether the video priors plus validators suffice for the claimed generalization, undermining the central empirical support.

Authors: The referee correctly identifies that the current §4 provides only high-level assertions without supporting quantitative tables, ablations, or specifics on the zero-shot test rigs. We will expand the experiments section in the revision to include: (i) quantitative metrics (e.g., plausibility scores, diversity measures, and direct comparisons to 4D-trained baselines), (ii) ablations isolating each validator component, and (iii) concrete details on the unseen topologies (species, bone-count differences from training rigs, and observed failure modes). These additions will directly address the verifiability concern. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation relies on external pretrained video model and independent geometric validators

full rationale

The paper's core derivation transfers motion priors from an external pretrained 2D video diffusion model into a rig-parameter distribution, then applies differentiable validators (self-intersection, joint limits) post-skinning. No quoted equations or steps reduce a claimed prediction to a fitted input by construction, nor does any load-bearing premise rest on a self-citation chain whose validity is internal to the paper. Zero-shot generalization to unseen topologies is presented as an empirical outcome of training rather than a definitional equivalence. The framework is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract only; no explicit free parameters, axioms, or invented entities are described. The approach implicitly assumes that video diffusion priors contain transferable 3D kinematic information and that geometric validators suffice for validity enforcement.

pith-pipeline@v0.9.0 · 5598 in / 1231 out tokens · 42827 ms · 2026-05-10T05:35:57.017466+00:00 · methodology

discussion (0)

Reference graph

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arXiv preprint arXiv:2601.06378 (2026) 5, 16

Zhang, H., Luo, J., Wan, B., Zhao, Y., Li, Z., Vasilkovsky, M., Wang, C., Wang, J., Ahuja, N., Zhou, B.: Rigmo: Unifying rig and motion learning for generative animation. arXiv preprint arXiv:2601.06378 (2026)

work page arXiv 2026
[57]

In: ICCV (2025)

Zhang, H., Xu, H., Feng, C., Jampani, V., Ahuja, N.: Physrig: Differentiable physics- based skinning and rigging framework for realistic articulated object modeling. In: ICCV (2025)

2025
[58]

In: The Twelfth International Conference on Learning Representations (ICLR) (2024)

Zhang, J., Huang, S., Tu, Z., Chen, X., Zhan, X., Yu, G., Shan, Y.: Tapmo: Shape- aware motion generation of skeleton-free characters. In: The Twelfth International Conference on Learning Representations (ICLR) (2024)

2024
[59]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Zhang, J., Weng, J., Kang, D., Zhao, F., Huang, S., Zhe, X., Bao, L., Shan, Y., Wang, J., Tu, Z.: Skinned motion retargeting with residual perception of motion semantics & geometry. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13864–13872 (2023)

2023
[60]

Motiondiffuse: Text-driven human motion generation with diffusion model

Zhang, M., Cai, Z., Pan, L., Hong, F., Guo, X., Yang, L., Liu, Z.: Motiondif- fuse: Text-driven human motion generation with diffusion model. arXiv preprint arXiv:2208.15001 (2022) ViPS: Video-informed Pose Spaces for Auto-Rigged Meshes Supplementary Material Honglin Chen1,2∗ , Karran Pandey3 , Rundi Wu1 , Matheus Gadelha2 , Yannick Hold-Geoffroy2 , Ayush...

work page arXiv 2022
[61]

EXACT SUBJECT : Use the pro vi de d object / species name as the subject ; do NOT change it to a d i f f e r e n t species / object
[62]

FULL - BODY / FULL - OBJECT ( CR IT IC AL ) : The ENTIRE subject must be visible from end to end ( e . g . , head - to - toe / nose - to - tail / top - to - bottom ) . A b s o l u t e l y NO cropping , NO cut - off limbs / ears / tail , NO partial framing . Keep the subject ce nt er ed with ge ner ou s margins
[63]

WIDE SHOT ( C RI TI CA L ) : Use a wide shot with the camera pulled back enough to g u a r a n t e e the whole subject fits c o m f o r t a b l y in frame , with extra space around it
[64]

NO ground plane , NO visible floor line , NO horizon

B A C K G R O U N D : Solid pure white studio b a c k g r o u n d . NO ground plane , NO visible floor line , NO horizon
[65]

Avoid harsh cast shadows ; keep r e f l e c t i o n s minimal

L IG HT IN G : Even , soft , s h a d o w l e s s studio li gh ti ng . Avoid harsh cast shadows ; keep r e f l e c t i o n s minimal
[66]

ViPS: Video-informed Pose Spaces – Supplementary Material 7

NO NEW E LE ME NTS : Do NOT i n t r o d u c e any other objects , humans , animals , props , text , logos , watermarks , scenery , or clutter . ViPS: Video-informed Pose Spaces – Supplementary Material 7
[67]

Use side or front three - quarter v i e w p o i n t ; AVOID direct front or rear views . D i v e r s i t y r e q u i r e m e n t s ( across prompts for the same subject ) : - Vary v i e w p o i n t ( side / front three - quarter ) , slight tilt angle ( e s p e c i a l l y for animals ) , and pose / o r i e n t a t i o n while ALWAYS keeping full - body / ...
[68]

Do NOT replace it with a d i f f e r e n t motion

** Use the P rov id ed Motion EXACTLY :** The prompt MUST des cr ib e the target motion f a i t h f u l l y . Do NOT replace it with a d i f f e r e n t motion . Do NOT add extra actions beyond what is stated
[69]

Do NOT invent new colors , accessories , markings , clothing , species , or extra objects

** Keep A p p e a r a n c e C O N S I S T E N T :** Use the pr ov id ed a p p e a r a n c e d e s c r i p t i o n as - is . Do NOT invent new colors , accessories , markings , clothing , species , or extra objects
[70]

If s o m e t h i n g is not m e n t i o n e d in the appearance , it must not appear in the prompt

** NO NEW EL EM EN TS ( VERY STRICT ) :** Do NOT i n t r o d u c e any new objects , humans , animals , props , accessories , text , logos , extra scenery items , or a d d i t i o n a l en ti tie s not e x p l i c i t l y present in the p ro vid ed a p p e a r a n c e d e s c r i p t i o n . If s o m e t h i n g is not m e n t i o n e d in the appearance ...
[71]

No new items in the scene

** B a c k g r o u n d & L ig ht in g :** Keep the b a c k g r o u n d simple and stable , c o n s i s t e n t with the p ro vid ed a p p e a r a n c e . No new items in the scene
[72]

pan " ,

** Camera Be ha vio r :** ST RI CT LY STATIC FULL BODY SHOT . Tripod - locked . The entire object visible at all times . ** Single shot , no transitions , no cuts .** NO camera m ov eme nt . Avoid words like " pan " , " zoom " , " track " , " dolly " , " close - up " , " follow " , " cut " , " scene change " , " t r a n s i t i o n " , " montage "
[73]

** Natural Physics :** Motion should have r e a l i s t i c weight and timing a p p r o p r i a t e to the object
[74]

Photorealistic , 4 k , high f id eli ty

** Style Default :** If no style is specified , default to " Photorealistic , 4 k , high f id eli ty ."
[75]

8 Honglin Chen et al

** Length :** 60 -90 words . 8 Honglin Chen et al. Output ONLY the final prompt . No quotes . No bullet points . No extra c o m m e n t a r y . Object ( appearance , keep exact ) : < appearance_text > Target Motion ( must use exact ) : < motion_text > Camera C o n s t r a i n t ( must use exact ) : Static Full Body Shot ( Tripod View ) . The entire object...