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REVIEW 3 major objections 5 minor 55 references

FastGrasp: Efficient Grasp Synthesis with Diffusion

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A one-stage diffusion model generates a MANO grasping pose in a single 0.14-second pass, without contact-map optimization, and beats two-stage baselines on physical plausibility and diversity.

desk verdict FastGrasp is a genuinely useful one-stage grasp synthesis method with real speed gains, but its physical-plausibility advantage is partly trained into the metric, and the evaluation lacks error bars. read the letter →

arxiv 2411.14786 v1 pith:3OY5TOAH submitted 2024-11-22 cs.RO cs.CV

Xiaofei Wu , Tao Liu , Caoji Li , Yuexin Ma , Yujiao Shi , Xuming He This is my paper · ORCID
classification cs.ROcs.CV
keywords graspsynthesisdiffusionmodellatenthand-objectinteractionMANOhandone-stagegenerationphysicalplausibilitydexterousgrasping
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that one-stage diffusion generation can replace the two-stage 'contact map plus iterative optimization' pipeline for synthesizing human hand grasps. The claim is that a latent diffusion model conditioned on an object point cloud, followed by a small adaptation module that enforces physical constraints, produces a full MANO hand pose in a single network pass — 0.14 seconds on an A40 GPU — while improving penetration volume, contact ratio, and diversity over ContactGen, GrabNet, HALO, and GraspTTA on GRAB, OakInk, and out-of-domain HO-3D. A sympathetic reader would care because the two-stage methods are accurate but slow (110 seconds for ContactGen), and the paper argues the speed gain comes without sacrificing — indeed with improving — physical plausibility and pose diversity.

What carries the argument

The machinery is a latent diffusion model with an asymmetric autoencoder and an adaptation module. The autoencoder maps 778 hand-mesh vertices into a 768-dimensional latent code and decodes that code to 61 MANO parameters, trained with reconstruction plus contact, penetration, and consistency losses. The diffusion model learns the latent distribution conditioned on a PointNet-encoded object point cloud, with DDIM for fast sampling; the adaptation module, an MLP that refines the sampled latent code before decoding, aligns the generated latent vector with the physical constraints of hand-object interaction. The two-step training (autoencoder frozen during diffusion; diffusion frozen during adaptation) is what makes the decoupling work.

What would settle it

Run the released model on a held-out set of objects and test each generated grasp in a physics engine of a different family than the one used for the displacement metric (for example, a contact-rich rigid-body simulator with joint friction), measuring whether the object stays in the hand under gravity, shaking, and a small external impulse. If many grasps that score well on the paper's voxelized-penetration and displacement metrics still drop or slip the object, the physical-plausibility advantage is metric-specific and would not transfer to real manipulation.

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Extended reading notes

Core claim

FastGrasp's central discovery is that a diffusion model operating in a compressed latent space of hand-mesh vertices, with the object represented as a PointNet embedding, can directly regress MANO grasp parameters, and that the missing physical constraint can be re-injected not by iterative optimization but by a learned adaptation module applied to the latent code before decoding. Trained in two steps — first an asymmetric autoencoder with reconstruction and contact-aware losses (contact map, penetration, consistency, from [21]), then a latent diffusion model, then the adaptation MLP — the pipeline decouples diversity (learned by the diffusion process) from physical plausibility (refined by the adaptation module). The paper reports that this single-pass design yields the lowest penetration volume on GRAB (1.25), OakInk (4.37), and out-of-domain HO-3D (5.23) among compared methods, at 0.14 seconds per grasp, while maintaining competitive or higher diversity as measured by entropy and cluster size.

Load-bearing premise

The load-bearing premise is that the contact-aware losses reused from earlier work, with their manually set weights, capture the same notion of physical correctness as the evaluation metrics, so that optimizing those losses genuinely improves grasp stability rather than only lowering a surrogate penetration score.

Editorial extensions

If this is right

  • FastGrasp generates a full MANO hand pose in 0.14 seconds on an A40 GPU, roughly 800 times faster than ContactGen's 110.6 seconds, making interactive and real-time applications feasible.
  • The one-stage design removes the dependency on intermediate contact maps, so the generated pose quality no longer suffers from accumulated errors in a first-stage representation.
  • On out-of-domain HO-3D objects, the model trained on GRAB or OakInk generalizes with penetration volume 5.23 or 12.30, respectively, better than GrabNet, GraspTTA, HALO, GF, and ContactGen, suggesting the learned physical constraints transfer to unseen objects.
  • The adaptation module alone accounts for a large reduction in penetration (on GRAB, from 8.43 to 1.25) while preserving the diffusion model's diversity, showing that physical plausibility and diversity can be decoupled in latent space.
  • Higher entropy and larger cluster sizes on OakInk indicate the one-stage generator produces a wider variety of natural grasps than the CV-AE-based baseline, not just a single canonical grip.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the penetration loss is genuinely contact-based rather than an artifact of the voxelization heuristic, the same architecture may port to four-finger or soft-robotic hand models by swapping the MANO decoder for another differentiable hand model and retraining the autoencoder.
  • Since the adaptation module is a small MLP added at the latent-code level, it could be applied to refine outputs from any latent-space generative model trained on hand-object data, such as a variational autoencoder or a flow-based model.
  • A direct test of the one-stage claim would be to compare FastGrasp's 0.14 seconds against a two-stage method whose optimization stage has been distilled or warm-started; if the two-stage method can be accelerated to comparable latency, the speed advantage narrows.
  • The physical-constraint losses are evaluated on penetration volume and simulator displacement; a natural extension is to measure whether the generated grasps withstand external perturbation (shaking or pushing) in a physics engine, which would test grasp robustness beyond static displacement.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper presents FastGrasp, a one-stage object-conditioned hand grasp synthesis method based on a latent diffusion model. Hand mesh vertices are encoded by a PointNet-based autoencoder into a latent code, a conditional denoising diffusion model is trained in this latent space with object point-cloud conditioning, and a lightweight MLP adaptation module refines the diffusion output before decoding to MANO parameters. This design avoids the two-stage contact-map-plus-iterative-optimization pipeline of prior work. The authors evaluate on GRAB, OakInk, and out-of-domain HO-3D, reporting penetration volume, simulation displacement, contact ratio, diversity (entropy and cluster size), inference time, and a user study; they report 0.14 s inference and improved physical metrics over GrabNet, GraspTTA, HALO, GF, and ContactGen.

Significance. If the results hold, the contribution is a practical speed/precision gain: a single network pass replaces iterative optimization, with a 0.14 s inference time versus 6.9-110.6 s for the compared methods, and the out-of-domain HO-3D evaluation is a useful generalization probe. The code release, the ablations showing the adaptation module's contribution, and the independent simulator-displacement signal are strengths. However, the physical-plausibility advantage is not yet independently established because the penetration-volume metric overlaps the training losses and no variance estimates are reported; the central claim therefore needs additional validation rather than a rewrite.

major comments (3)
  1. [§3.2, Eqs. (4)-(7) and §4.2] The penetration-volume metric is the same geometric notion that is directly minimized by Lpenetr during autoencoder training (lambda4=10) and adaptation-module training (lambda_d4=20), with Lcmap and Lconsist further shaping contact in ways that overlap the contact-ratio metric. Consequently, the lower penetration volumes in Tables 2 and 3 (e.g., 1.25 vs 2.16 on GRAB; 5.23 vs 9.96 on HO-3D) are partly fitted quantities, not independent evidence of physical plausibility. The simulation-displacement metric is the natural independent check and does favor FastGrasp over ContactGen on all three benchmarks, but the manuscript gives no error bars, simulator configuration, or per-object breakdown for it. I therefore cannot yet verify the load-bearing claim that the reported superiority reflects genuine physical plausibility rather than heuristic-fitting. Please add seed/run variance, simulator details, and at least one physical metric that was not used in the training loss.
  2. [Tables 2 and 3, and §4.2 diversity assessment] No error bars or significance tests are reported, and several headline margins are small (e.g., OakInk entropy 2.92 vs 2.88, cluster size 4.96 vs 4.07; HO-3D entropy 2.88 vs 2.81). The diversity metrics additionally depend on the number of generated grasps per object and on the K-means initialization, neither of which is specified. Please report mean +/- standard deviation over multiple runs or seeds, state the number of grasps generated per object, and include per-object or per-category breakdowns, especially for the single HO-3D out-of-domain split.
  3. [§3.2, Eqs. (4)-(6), and Supplementary §7-8] The physical losses Lconsist, Lcmap, and Lpenetr are said to be taken from [21], but their functional forms are not defined in the paper or the supplementary material; the supplement only restates their purpose. Because these terms carry weights lambda3=1000, lambda4=10, lambda5=10 and are central to the physical-plausibility story, the exact definitions, discretization, and point/mesh correspondence are needed for reproducibility and for assessing the metric-overlap concern. The adaptation-module training protocol should also be specified, in particular whether the diffusion model and decoder are frozen during adaptation training and how z1 is sampled for the adaptation step.
minor comments (5)
  1. [§4 intro and §4.5] The text repeatedly refers to 'Sec. 8' for comparisons (e.g., 'we compare our method with the recent state-of-the-art approaches (Sec. 8)'), but the main text has no Section 8; the comparisons appear in Tables 2-3. Please renumber or fix these cross-references.
  2. [Table 1 and §4.3] The HO-3D row of Table 1 has 'physical loss12.73' with a missing space, and §4.3 says 'bath size = 256' instead of 'batch size = 256'.
  3. [§4.6, Fig. 6] The user study reports only aggregate percentages in Fig. 6; please provide the full protocol, including the number of participants who rated each object, the exact question asked, and whether the differences are statistically significant. A study with ten participants, ten objects, and three grasps per method is too small to support a strong perceptual claim without such details.
  4. [Table 3 and Supplement Table 4] In the OakInk block of Table 3, the method label 'GrabNet [49]' should probably be 'GrabNet [44]' since OakInk is [49]; also, Supplement Table 4 reports 'Penetration Distance' and 'Penetration Volume' without defining the former metric.
  5. [§3.3, Eqs. (9)-(11)] It is unclear why z2 is added to z1 before decoding rather than concatenated or otherwise fused, and whether the decoder was frozen during adaptation training; a sentence explaining this design choice would improve reproducibility.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the core one-stage diffusion pipeline is independently evaluated; the only self-citation is non-load-bearing, and the penetration-loss/metric overlap is ordinary optimization, not a constructional identity.

full rationale

The claimed derivation chain is self-contained relative to standard train/evaluation practice. The only potentially circular-looking element is that Lcmap and Lpenetr (Eqs. 5-6, Sec. 3.2) train the model to maintain contact and prevent penetration, while Sec. 4.2 evaluates contact ratio and penetration volume. However, the paper never defines the losses as identical to the voxelization metric, the evaluation is on held-out objects, and the baseline methods are optimized with similar geometric objectives; this is loss-metric alignment, not a prediction forced by construction. Independent evidence anchors the main claims: simulation displacement is not a training loss, diversity/entropy measures stochastic spread rather than a fitted target, out-of-domain HO-3D tests generalization, and a user study compares naturalness. The single self-citation (Ref. [33], RealDex, with author overlap X. Wu) appears in a related-work list and bears no load in the method or experiments. The paper does have evidential weaknesses: the Supplement promises details of the borrowed losses but gives no formulas, and no error bars or simulator protocol are reported; these are correctness concerns, not circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on hand-tuned loss weights and latent-space hyperparameters, plus domain assumptions about MANO expressiveness, PointNet conditioning quality, and the physical-loss proxies from [21]. No new unobserved entities are introduced.

free parameters (4)
  • loss weights lambda_1..lambda_5 and lambda^d_1..lambda^d_5 = 0.1, 1, 1000, 10, 10; 100, 0.1, 1000, 20, 0.1
    Hand-chosen weights balancing reconstruction vs contact/penetration losses; directly control physical plausibility of results and are tuned on the evaluation datasets.
  • latent dimension Nz = 768
    Architecture choice for latent hand representation; affects capacity and diversity.
  • number of object points No = 3000
    Point cloud sampling density used for conditioning; chosen by hand.
  • DDIM denoising steps N = not reported
    Number of sampling steps is not given in the main text; it directly trades speed against quality and is required to reproduce the 0.14s timing.
assumptions (4)
  • domain assumption The MANO parametric hand model spans the space of natural hand grasps.
    Used as the output representation (Sec. 3.2); if MANO cannot express certain grasp styles, the model cannot generate them.
  • domain assumption The physical loss functions Lconsist, Lcmap, Lpenetr from [21] correctly quantify hand-object interaction quality.
    Adopted in Eq. 4-6; the paper's physical-plausibility results rest on these losses being faithful proxies.
  • domain assumption PointNet embeddings of object point clouds provide sufficient conditioning for grasp generation.
    Object shape is encoded only through PointNet (Sec. 3.2); poor shape encoding would limit generalization.
  • standard math DDPM/DDIM theory provides a valid generative model for the latent hand distribution.
    Standard diffusion assumptions, cited to [17, 43]; no new theory is developed.

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Cite this review

Pith. "Pith review of FastGrasp: Efficient Grasp Synthesis with Diffusion." pith.science (2026). https://pith.science/paper/3OY5TOAH

@misc{pith2026241114786,
  author       = {Pith},
  title        = {Pith review of: FastGrasp: Efficient Grasp Synthesis with Diffusion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3OY5TOAH}},
  note         = {Machine review of arXiv:2411.14786}
}
read the original abstract

Effectively modeling the interaction between human hands and objects is challenging due to the complex physical constraints and the requirement for high generation efficiency in applications. Prior approaches often employ computationally intensive two-stage approaches, which first generate an intermediate representation, such as contact maps, followed by an iterative optimization procedure that updates hand meshes to capture the hand-object relation. However, due to the high computation complexity during the optimization stage, such strategies often suffer from low efficiency in inference. To address this limitation, this work introduces a novel diffusion-model-based approach that generates the grasping pose in a one-stage manner. This allows us to significantly improve generation speed and the diversity of generated hand poses. In particular, we develop a Latent Diffusion Model with an Adaptation Module for object-conditioned hand pose generation and a contact-aware loss to enforce the physical constraints between hands and objects. Extensive experiments demonstrate that our method achieves faster inference, higher diversity, and superior pose quality than state-of-the-art approaches. Code is available at \href{https://github.com/wuxiaofei01/FastGrasp}{https://github.com/wuxiaofei01/FastGrasp.}

Figures

Figures reproduced from arXiv: 2411.14786 by the authors.

Figure 1
Figure 1. FastGrasp provides extensive realistic grasping of dexterous hands synchronized with human poses. Abstract Effectively modeling the interaction between human hands and objects is challenging due to the complex phys￾ical constraints and the requirement for high generation efficiency in applications. Prior approaches often employ computationally intensive two-stage approaches, which first generate an intermediate repr… view at source ↗
Figure 2
Figure 2. Model training architecture. We divide the training process into two parts. In the first part, we use a latent diffusion model to generate grasping poses from object point clouds. However, the diffusion model struggles to directly learn the physical constraints between the hand and object, leading to issues such as penetration and displacement. To address this, the second part involves training an Adaptation Module … view at source ↗
Figure 3
Figure 3. Model inference architecture. We start by inputting Gaussian noise and the object’s point cloud into the model. The diffusion model then generates hand representations in latent space. The Adaptation Module refines these representations, which are then decoded into MANO parameters. Finally, we construct the hand mesh using the MANO layer. The goal of incorporating hand-object physical con￾straints is to ensure that … view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison between our method and Ours w/o Adaptation Module (AM). Starting from the same random Gaussian [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Qualitative comparisons with state-of-the-art methods on GRAB, OakInk, and HO-3D datasets. Each pair (two columns) [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: User study results. The numbers indicate the percentage [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: To assess the impact of a physically constrained loss function, we compare model performance with and without it. Each pair of [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: To evaluate the necessity of hand vertices as inputs, we visualize the model’s output using both hand parameters and hand [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: In the visualization results of the autoencoder, we selected two different grasping poses for each object, each shown from two [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: In the autoencoder visualization results, we randomly selected grasping poses, each shown from two different perspectives. [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]

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Reference graph

Works this paper leans on

55 extracted references · 42 canonical work pages

  1. [21]

    Hand-object contact consistency reasoning for human grasps generation

    Hanwen Jiang, Shaowei Liu, Jiashun Wang, and Xiaolong Wang. Hand-object contact consistency reasoning for human grasps generation. In Proceedings of the IEEE/CVF interna- tional conference on computer vision , pages 11107–11116,

  2. [1]

    Kemp, and James Hays

    Samarth Brahmbhatt, Cusuh Ham, Charles C. Kemp, and James Hays. Contactdb: Analyzing and predicting grasp contact via thermal imaging. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8701–8711, 2019. 2

    work page 2019

  3. [2]

    Contactgrasp: Functional multi-finger grasp synthe- sis from contact

    Samarth Brahmbhatt, Ankur Handa, James Hays, and Dieter Fox. Contactgrasp: Functional multi-finger grasp synthe- sis from contact. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2386–2393,

    work page 2019

  4. [3]

    ContactPose: A Dataset of Grasps with Object Contact and Hand Pose

    Samarth Brahmbhatt, Chengcheng Tang, Christopher D. Twigg, Charles C. Kemp, and James Hays. Contactpose: A dataset of grasps with object contact and hand pose. ArXiv, abs/2007.09545, 2020. 2

    work page Pith review arXiv 2007

  5. [4]

    Text2HOI: Text-guided 3D Motion Generation for Hand-Object Interaction

    Junuk Cha, Jihyeon Kim, Jae Shin Yoon, and Seungryul Baek. Text2hoi: Text-guided 3d motion generation for hand- object interaction. ArXiv, abs/2404.00562, 2024. 1

    work page Pith review arXiv 2024

  6. [5]

    Narang, Karl Van Wyk, Umar Iqbal, Stan Birchfield, Jan Kautz, and Dieter Fox

    Yu-Wei Chao, Wei Yang, Yu Xiang, Pavlo Molchanov, Ankur Handa, Jonathan Tremblay, Yashraj S. Narang, Karl Van Wyk, Umar Iqbal, Stan Birchfield, Jan Kautz, and Dieter Fox. Dexycb: A benchmark for capturing hand grasp- ing of objects. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 9040–9049,

    work page 2021

  7. [6]

    AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object Reconstruction

    Zerui Chen, Yana Hasson, Cordelia Schmid, and Ivan Laptev. Alignsdf: Pose-aligned signed distance fields for hand-object reconstruction. ArXiv, abs/2207.12909, 2022. 1

    work page Pith review arXiv 2022

  8. [7]

    gsdf: Geometry-driven signed distance functions for 3d hand-object reconstruction

    Zerui Chen, Shizhe Chen, Cordelia Schmid, and Ivan Laptev. gsdf: Geometry-driven signed distance functions for 3d hand-object reconstruction. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 12890–12900, 2023. 1

    work page 2023

Show all 55 references
  1. [8]

    Aleny `a, Francesc Moreno-Noguer, and Grgory Rogez

    Enric Corona, Albert Pumarola, G. Aleny `a, Francesc Moreno-Noguer, and Grgory Rogez. Ganhand: Predict- ing human grasp affordances in multi-object scenes. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5030–5040, 2020. 2

    2020

  2. [9]

    Human- computer interaction : Interact 2005 : Ifip tc13 international conference, rome, italy, september 12-16, 2005 : proceed- ings

    Maria Francesca Costabile and Fabio Patern `o. Human- computer interaction : Interact 2005 : Ifip tc13 international conference, rome, italy, september 12-16, 2005 : proceed- ings. 2005. 1

    2005

  3. [10]

    Renaud Detry, Dirk Kraft, Anders Glent Buch, Norbert Kr¨uger, and Justus H. Piater. Refining grasp affordance mod- els by experience. 2010 IEEE International Conference on Robotics and Automation, pages 2287–2293, 2010. 2

    2010

  4. [11]

    Crandall

    Bardia Doosti, Shujon Naha, Majid Mirbagheri, and David J. Crandall. Hope-net: A graph-based model for hand-object pose estimation. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 6607–6616,

    2020

  5. [12]

    Vision-based pose estimation for robot- mediated hand telerehabilitation

    Giuseppe Air `o Farulla, Daniele Pianu, Marco Cempini, Mario Cortese, Ludovico Orlando Russo, Marco Indaco, Roberto Nerino, Antonio Chimienti, Calogero Maria Oddo, and Nicola Vitiello. Vision-based pose estimation for robot- mediated hand telerehabilitation. Sensors (Basel, Sw...

    2016

  6. [13]

    Slusallek

    Anindita Ghosh, Rishabh Dabral, Vladislav Golyanik, Chris- tian Theobalt, and P. Slusallek. Imos: Intent-driven full-body motion synthesis for human-object interactions. Computer Graphics Forum, 42, 2022. 2

    2022

  7. [14]

    Twigg, Minh V o, Samarth Brahmbhatt, and Charles C

    Patrick Grady, Chengcheng Tang, Christopher D. Twigg, Minh V o, Samarth Brahmbhatt, and Charles C. Kemp. Contactopt: Optimizing contact to improve grasps. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1471–1481, 2021. 2

    2021

  8. [15]

    Honnotate: A method for 3d annotation of hand and object poses

    Shreyas Hampali, Mahdi Rad, Markus Oberweger, and Vin- cent Lepetit. Honnotate: A method for 3d annotation of hand and object poses. In 2020 IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), 2020. 2, 5, 6, 7, 8

    2020

  9. [16]

    Black, Ivan Laptev, and Cordelia Schmid

    Yana Hasson, G ¨ul Varol, Dimitrios Tzionas, Igor Kale- vatykh, Michael J. Black, Ivan Laptev, and Cordelia Schmid. Learning joint reconstruction of hands and manipulated ob- jects. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11799–11808, 2019. 2

    2019

  10. [17]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. 2, 4

    2020

  11. [18]

    Efficient physics-based implementation for re- alistic hand-object interaction in virtual reality

    Markus H ¨oll, Markus Oberweger, Clemens Arth, and Vin- cent Lepetit. Efficient physics-based implementation for re- alistic hand-object interaction in virtual reality. 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pages 175–182, 2018. 1

    2018

  12. [19]

    Imitation learning of whole-body grasps

    Kaijen Hsiao and Tomas Lozano-Perez. Imitation learning of whole-body grasps. 2006 IEEE/RSJ International Con- ference on Intelligent Robots and Systems, pages 5657–5662,

    2006

  13. [20]

    Hand-object contact consistency reasoning for human grasps generation

    Hanwen Jiang, Shaowei Liu, Jiashun Wang, and Xiaolong Wang. Hand-object contact consistency reasoning for human grasps generation. 2021 IEEE/CVF International Confer- ence on Computer Vision (ICCV), pages 11087–11096, 2021. 2

    2021

  14. [22]

    Black, Krikamol Muandet, and Siyu Tang

    Korrawe Karunratanakul, Jinlong Yang, Yan Zhang, Michael J. Black, Krikamol Muandet, and Siyu Tang. Grasping field: Learning implicit representations for hu- man grasps. In 2020 International Conference on 3D Vision (3DV), 2020. 6, 7

    2020

  15. [23]

    A skeleton-driven neural occupancy representation for articulated hands

    Korrawe Karunratanakul, Adrian Spurr, Zicong Fan, Otmar Hilliges, and Siyu Tang. A skeleton-driven neural occupancy representation for articulated hands. In 2021 International Conference on 3D Vision (3DV), pages 11–21. IEEE, 2021. 1, 2, 5, 6, 7, 8

    2021

  16. [24]

    Kry and Dinesh K

    Paul G. Kry and Dinesh K. Pai. Interaction capture and syn- thesis. ACM SIGGRAPH 2006 Papers, 2005. 2

    2006

  17. [25]

    Fouhey, and Leonidas J

    Nilesh Kulkarni, Davis Rempe, Kyle Genova, Abhijit Kundu, Justin Johnson, David F. Fouhey, and Leonidas J. Guibas. Nifty: Neural object interaction fields for guided human motion synthesis. ArXiv, abs/2307.07511, 2023. 2

    2023 arXiv

  18. [26]

    Diffu- sion models already have a semantic latent space

    Mingi Kwon, Jaeseok Jeong, and Youngjung Uh. Diffu- sion models already have a semantic latent space. ArXiv, abs/2210.10960, 2022. 3

    2022 arXiv

  19. [27]

    Task-oriented human-object interactions generation with im- plicit neural representations

    Quanzhou Li, Jingbo Wang, Chen Change Loy, and Bo Dai. Task-oriented human-object interactions generation with im- plicit neural representations. 2024 IEEE/CVF Winter Con- ference on Applications of Computer Vision (WACV), pages 3023–3032, 2023. 2

    2024

  20. [28]

    Li, Jiaxin L

    Y . Li, Jiaxin L. Fu, and Nancy S. Pollard. Data-driven grasp synthesis using shape matching and task-based prun- ing. IEEE Transactions on Visualization and Computer Graphics, 13:732–747, 2007. 2

    2007

  21. [29]

    Miao Liu, Siyu Tang, Yin Li, and James M. Rehg. Forecast- ing human object interaction: Joint prediction of motor atten- tion and egocentric activity. ArXiv, abs/1911.10967, 2019. 2

    1911 arXiv

  22. [30]

    Tenenbaum

    Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B. Tenenbaum. Compositional visual generation with composable diffusion models. ArXiv, abs/2206.01714, 2022. 2

    2022 arXiv

  23. [31]

    Contactgen: Generative contact modeling for grasp generation

    Shaowei Liu, Yang Zhou, Jimei Yang, Saurabh Gupta, and Shenlong Wang. Contactgen: Generative contact modeling for grasp generation. In Proceedings of the IEEE/CVF In- ternational Conference on Computer Vision , pages 20609– 20620, 2023. 1, 2, 5, 6, 7, 8

    2023

  24. [32]

    Semi-supervised 3d hand-object poses estima- tion with interactions in time

    Shao-Wei Liu, Hanwen Jiang, Jiarui Xu, Sifei Liu, and Xi- aolong Wang. Semi-supervised 3d hand-object poses estima- tion with interactions in time. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14682–14692, 2021. 1, 2

    2021

  25. [33]

    Realdex: Towards human- like grasping for robotic dexterous hand

    Yumeng Liu, Yaxun Yang, Youzhuo Wang, Xiaofei Wu, Ji- amin Wang, Yichen Yao, S ¨oren Schwertfeger, Sibei Yang, Wenping Wang, Jingyi Yu, et al. Realdex: Towards human- like grasping for robotic dexterous hand. arXiv preprint arXiv:2402.13853, 2024. 2, 6

    2024 arXiv

  26. [34]

    Controllable mesh generation through sparse latent point diffusion models

    Zhaoyang Lyu, Jinyi Wang, Yuwei An, Ya Zhang, Dahua Lin, and Bo Dai. Controllable mesh generation through sparse latent point diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 271–280, 2023. 3

    2023

  27. [35]

    Pollard and Victor B

    Nancy S. Pollard and Victor B. Zordan. Physically based grasping control from example. In Symposium on Computer Animation, 2005. 2

    2005

  28. [36]

    Barron, and Ben Milden- hall

    Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Milden- hall. Dreamfusion: Text-to-3d using 2d diffusion. ArXiv, abs/2209.14988, 2022. 2

    2022 arXiv

  29. [37]

    Diffusion autoen- coders: Toward a meaningful and decodable representation

    Konpat Preechakul, Nattanat Chatthee, Suttisak Wizad- wongsa, and Supasorn Suwajanakorn. Diffusion autoen- coders: Toward a meaningful and decodable representation. 2022 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 10609–10619, 2021. 2, 3

    2022

  30. [38]

    Qi, Hao Su, Kaichun Mo, and Leonidas J

    C. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. Point- net: Deep learning on point sets for 3d classification and seg- mentation. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 77–85, 2016. 3, 4

    2017

  31. [39]

    High-resolution image syn- thesis with latent diffusion models, 2021

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj¨orn Ommer. High-resolution image syn- thesis with latent diffusion models, 2021. 4

    2021

  32. [40]

    Em- bodied hands: Modeling and capturing hands and bodies to- gether

    Javier Romero, Dimitrios Tzionas, and Michael J Black. Em- bodied hands: Modeling and capturing hands and bodies to- gether. arXiv preprint arXiv:2201.02610, 2022. 2, 4, 5

    2022 arXiv

  33. [41]

    Weiss, Niru Ma- heswaranathan, and Surya Ganguli

    Jascha Narain Sohl-Dickstein, Eric A. Weiss, Niru Ma- heswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. ArXiv, abs/1503.03585, 2015. 2

    2015 arXiv

  34. [42]

    Learning structured output representation using deep conditional gen- erative models

    Kihyuk Sohn, Honglak Lee, and Xinchen Yan. Learning structured output representation using deep conditional gen- erative models. In Advances in Neural Information Process- ing Systems. Curran Associates, Inc., 2015. 1

    2015

  35. [43]

    Denois- ing diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denois- ing diffusion implicit models. arXiv:2010.02502, 2020. 5

    2010 arXiv

  36. [44]

    Grab: A dataset of whole-body human grasp- ing of objects

    Omid Taheri, Nima Ghorbani, Michael J Black, and Dim- itrios Tzionas. Grab: A dataset of whole-body human grasp- ing of objects. In Computer Vision–ECCV 2020: 16th Eu- ropean Conference, Glasgow, UK, August 23–28, 2020, Pro- ceedings, Part IV 16, pages 581–600. Springer, 2020....

    2020

  37. [45]

    H+o: Uni- fied egocentric recognition of 3d hand-object poses and in- teractions

    Bugra Tekin, Federica Bogo, and Marc Pollefeys. H+o: Uni- fied egocentric recognition of 3d hand-object poses and in- teractions. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4506–4515, 2019. 2

    2019

  38. [46]

    Capturing hands in action using discriminative salient points and physics sim- ulation

    Dimitrios Tzionas, Luca Ballan, Abhilash Srikantha, Pablo Aponte, Marc Pollefeys, and Juergen Gall. Capturing hands in action using discriminative salient points and physics sim- ulation. International Journal of Computer Vision, 118:172– 193, 2015. 1

    2015

  39. [47]

    Novel view synthesis with diffusion models.ArXiv, abs/2210.04628, 2022

    Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, and Mohammad Norouzi. Novel view synthesis with diffusion models.ArXiv, abs/2210.04628, 2022. 2

    2022 arXiv

  40. [48]

    Saga: Stochastic whole- body grasping with contact

    Yan Wu, Jiahao Wang, Yan Zhang, Siwei Zhang, Otmar Hilliges, Fisher Yu, and Siyu Tang. Saga: Stochastic whole- body grasping with contact. In European Conference on Computer Vision, pages 257–274. Springer, 2022. 2, 6

    2022

  41. [49]

    OakInk: A large-scale knowledge reposi- tory for understanding hand-object interaction

    Lixin Yang, Kailin Li, Xinyu Zhan, Fei Wu, Anran Xu, Liu Liu, and Cewu Lu. OakInk: A large-scale knowledge reposi- tory for understanding hand-object interaction. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 5, 6, 7, 8

    2022

  42. [50]

    Affordance diffusion: Synthesizing hand-object interactions

    Yufei Ye, Xueting Li, Abhi Gupta, Shalini De Mello, Stan Birchfield, Jiaming Song, Shubham Tulsiani, and Sifei Liu. Affordance diffusion: Synthesizing hand-object interactions. 2023 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 22479–22489, 2023. 2

    2023

  43. [51]

    Manipnet

    He Zhang, Yuting Ye, Takaaki Shiratori, and Taku Komura. Manipnet. ACM Transactions on Graphics (TOG) , 40:1 – 14, 2021. 2

    2021

  44. [52]

    Cams: Canonicalized manipulation spaces for category-level functional hand-object manipulation synthe- sis

    Juntian Zheng, Qingyuan Zheng, Lixing Fang, Yun Liu, and Li Yi. Cams: Canonicalized manipulation spaces for category-level functional hand-object manipulation synthe- sis. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 585–594, 2023. 2

    2023

  45. [53]

    Toch: Spatio-temporal object-to-hand correspondence for motion refinement

    Keyang Zhou, Bharat Lal Bhatnagar, Jan Eric Lenssen, and Gerard Pons-Moll. Toch: Spatio-temporal object-to-hand correspondence for motion refinement. In European Con- ference on Computer Vision, 2022. 2 FastGrasp: Efficient Grasp Synthesis with Diffusion Supplementary Material...

    2022

  46. [54]

    Overview of Material The supplementary material comprehensively details our experiments, results, and visualizations. Tab. 4 examines the impact of physical constraints during autoencoder train- ing and compares the effects of hand verts versus hand pa- rameters as inputs. Sec...

  47. [55]

    More Autoencoder Experimental Results In training the autoencoder, we use hand vertices as input and apply both reconstruction and physical loss functions. Sec. 8.1 and Sec. 8.2 examine the effects of training the model with hand vertices and reconstruction loss alone ver- sus...

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.