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REVIEW 3 major objections 5 minor 56 references

Human Grasp Generation for Rigid and Deformable Objects with Decomposed VQ-VAE

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Decomposing the hand into fingers and palm, and decoding posture before position, produces higher-quality grasps and more accurate deformation simulation, with about 14.1% average improvement over prior methods.

desk verdict The deformable-object components are genuinely new and the HMDO gains look real, but the Quality Index is calibrated on test benchmarks and the 'four benchmarks' superiority claim does not survive a crossover analysis. read the letter →

arxiv 2501.05483 v1 pith:EHAEMMLA submitted 2025-01-08 cs.RO cs.GR

Mengshi Qi , Zhe Zhao , Huadong Ma This is my paper · ORCID
classification cs.ROcs.GR
keywords graspgenerationVQ-VAEdecomposedarchitecturehand-objectinteractiondeformableobjectsmeshtransformerMANOhandmodelnormalvectorpositionencoding
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

Humans grasp objects with a hand made of semi-independent parts, yet most grasp models encode the hand as one whole and predict its pose in a single step. This paper argues that decomposing the hand into five fingers and a palm, giving each its own discrete codebook, and then generating the grasp in two stages—posture first, position second—produces more realistic and more varied grasps. For deformable objects, the proposal adds a mesh-understanding network that uses surface normal directions to compute where a hand presses into the object and to simulate the resulting deformation. The authors report that this design raises grasp quality by about 14.1% on average over previous methods across four benchmarks, improves hand-object contact distance on deformable objects, and increases grasp diversity. If correct, the paper shows that respecting the part structure of the hand and the order of grasp decisions matters more than a single bigger network.

What carries the argument

The load-bearing mechanism is the decomposed, multi-codebook VQ-VAE: the hand's five fingers and palm are each quantized against their own codebook, and an autoregressive model generates the index sequence conditioned on the object codebook, preserving the discrete, part-determined structure of grasps. Around this core sit the dual-stage decoder (posture parameters via MANO under skeletal angle gating, then position parameters) and, for deformable objects, Mesh UFormer, a symmetric mesh encoder–decoder that downsamples the mesh by voxel clustering, propagates features between resolutions through KD-tree point mappings, and uses a normal-vector-guided position encoding inside its attention. The hand-object contact quantification completes the mechanism: it computes both hand-to-object and object-to-hand penetration distances using normal-vector dot products and ray casting, and those distances are fed to the deformation network.

What would settle it

Recompute the quality index using a weight learned only from the training split of Obman (or set to a=0.5) and re-run the comparisons on HO-3D, FPHA, and GRAB; if the relative improvement over GraspTTA and ContactGen disappears or reverses, the central claim fails. Alternatively, check whether human preference ratings or physical simulation success track the quality index when the weight is varied — if small changes in a flip the method ranking, the claimed improvement is metric-dependent.

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Extended reading notes

Core claim

The central claim is that a part-aware discrete latent representation, paired with an ordered two-stage decoder, yields more realistic and more adaptable grasps. Concretely, DVQ-VAE-2 divides the hand into five fingers and a palm, maps each part to its own VQ-VAE codebook (a learned discrete dictionary), and encodes the object with two separate encoders, one for grasp type and one for grasp pose. Decoding is sequential: a PixelCNN autoregressively predicts the hand codebook indices conditioned on the object codebook, then a posture decoder produces MANO posture parameters under skeletal joint-angle gating, and a position decoder then produces the remaining position parameters. For deformable objects, Mesh UFormer replaces PointNet as the backbone, using voxel-based hierarchical mesh sampling and normal-vector-guided position encoding to predict per-vertex deformation from bidirectional hand-object penetration distances. The authors report improvements in Quality Index of 13.3%, 13.7%, and 1.2% on HO-3D, FPHA, and GRAB with the PointNet backbone, and further gains of 5.86% to 20.43% in Quality Index over PointNet on rigid datasets and 2.23% in Hand-object Contact Distance over PointTransformerV3 on the HMDO dataset.

Load-bearing premise

The paper's headline result depends on the assumption that the single weight balancing penetration volume against grasp displacement in its quality index, chosen using the Obman and GRAB datasets, is a stable property of grasp quality across all four benchmarks.

Editorial extensions

If this is right

  • Grasp generation becomes much faster: DVQ-VAE-2 generates a batch of grasps in 0.11 s on a single GPU, compared with roughly 19–20 s for GraspTTA and over 100 s for ContactGen, while matching or exceeding their contact ratios.
  • The part-aware codebooks increase grasp diversity: cluster size on HO-3D rises from about 0.81 for GraspCVAE/GraspTTA to roughly 3.86, so a single object yields a wider range of natural postures without test-time adaptation.
  • For deformable objects, deformation accuracy improves specifically at hand-contact regions (2.23% better Hand-object Contact Distance than PointTransformerV3) and unwanted far-field deformation drops sharply (a 68.82% relative improvement in Chamfer Distance), using only about 14.56% of PointTransformerV3's parameters.
  • The dual-stage decoding strategy alone improves the Quality Index by 45.2% for DVQ-VAE and 47.6% for VQ-VAE in the ablations, implying that the ordering of posture-then-position is itself a substantial source of the gain.
  • The learned position decoder can refine grasps from other methods: applying it to grasps produced by other models reduces penetration, as shown in Figure 10.

Reading between the lines

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

  • A direct robustness check suggested by the metric design: recalibrate the quality index weight using only the training partition of Obman and see whether the 14.1% average improvement persists; if it shrinks, the headline number is partly test-set dependent, even though raw penetration and displacement improvements may remain.
  • The part-level codebooks could transfer to robot hands with different morphologies: treating each finger as a conditionable module would let the same framework generate grasps for hands with varying finger lengths or counts by swapping or adding codebooks.
  • The normal-vector-guided position encoding is a general inductive bias for contact-driven mesh deformation, so it could be applied to other soft-body manipulation problems where local surface orientation determines how the material yields.
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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. The paper proposes DVQ-VAE-2, a decomposed VQ-VAE for generating human grasps on rigid and deformable objects. The hand is encoded as six separate components (five fingers and a palm), each with its own codebook, and decoding is split into a posture stage and a position stage. For deformable objects, the authors introduce a Mesh UFormer backbone that operates on voxelized mesh hierarchies, together with a normal-vector-guided position encoding and a hand-object contact quantification scheme that feeds penetration distances into the deformation network. Experiments compare against GraspCVAE, GraspTTA, ContactGen, GraspingField, and PointTransformerV3 on Obman, HO-3D, FPHA, GRAB, and HMDO, with the central claim being a roughly 14.1% relative improvement in grasp quality over state-of-the-art methods across four benchmarks.

Significance. The architectural ideas are plausible and potentially useful: part-aware discrete latent codes for hands, a dual-stage decoding strategy, and a mesh-based hierarchical backbone for deformation are all reasonable directions, and the paper reports extensive ablations. A clear strength is that raw penetration volume and grasp displacement numbers are reported for each method, and the authors state that source code and models will be released, which supports reproducibility. However, the headline evaluation rests on a new Quality Index whose weighting parameter is calibrated using test-set statistics, and the reported improvement numbers are internally inconsistent. Until the evaluation metric is fixed or the claims are reformulated to rest on the raw metrics, the central superiority claim is not supported.

major comments (3)
  1. [§5.2.1, Eq. (39), Table 1] The headline Quality Index is calibrated on test sets. The weight a=0.301 is computed on the Obman and GRAB datasets, while GRAB is one of the four test sets in Table 1, so the primary comparison metric is partly fit to the evaluation distribution. The problem is not merely formal: using the reported values, DVQ-VAE-2 beats GraspCVAE on GRAB only when a<0.526 and on Obman only when a>0.862, so no single scalar a can make the method better than the best baseline on both datasets simultaneously. Table 1 itself shows that with a=0.301, DVQ-VAE-2 has Quality Index 2.89 on Obman versus GraspCVAE's 2.57. The 'four benchmarks' superiority claim and the 14.1% aggregate therefore need to be reformulated; the raw penetration and displacement numbers should be the primary evidence, or the authors should report robustness of the ranking over a range of a values, or fix a without using test-set statistics.
  2. [Abstract and §5.5, Table 1] The headline improvement numbers are internally inconsistent. The abstract claims 'approximately 14.1%' relative improvement in grasp quality across four benchmarks, but §5.5 reports 13.3% on HO-3D, 13.7% on FPHA, and 1.2% on GRAB for DVQ-VAE, and then reports for DVQ-VAE-2 a 5.86% to 20.43% improvement over PointNet, not over state-of-the-art methods. No calculation in the paper derives 14.1% from Table 1, and the comparison basis changes between 'state-of-the-art' and 'PointNet backbone.' Please clarify what exactly is averaged and against which baseline.
  3. [Table 4] The ablation table contains a duplicated row label with conflicting numbers: 'DVQ-VAE + Dual-Stage (Two Encoders)' appears with Penetration 4.44, Grasp Disp 3.61, Quality Index 3.86, and again with Penetration 5.36, Grasp Disp 2.75, Quality Index 3.54. This makes it impossible to infer which configuration corresponds to the reported results and undermines the ablation claims for the dual-stage strategy. Please assign unique names to each variant and reconcile the numbers.
minor comments (5)
  1. [§3.5, Eq. (17), §5.4] The negative weight λm=-50 on Lm is never explained. Since Lm as defined in Eq. (14) is a similarity (larger overlap is better), a negative coefficient is mathematically coherent if the loss is to be minimized; however, the text calls it a 'contact map consistency loss' and lists it under losses to improve interaction. Please state the sign convention explicitly and verify that the total loss is bounded in a sensible range.
  2. [§5.5] The model name is written as 'D-VQVAE-2' in one place ('After integrating Mesh UFormer, we propose D-VQVAE-2'); please make the nomenclature consistent throughout.
  3. [Table 2] The first row labeled 'Originality' has no method name and reports Chamfer Distance 0.00; it should be identified as the un-deformed input mesh or removed, since it is not a compared method.
  4. [Table 1 and §5.3] Table 1 does not state that ContactGen is trained on GRAB while the other methods are trained on Obman; this makes the GRAB comparison in-domain for ContactGen, which should be noted when interpreting the results.
  5. [§5.2.1] The Quality Index is introduced with 'lower is better,' but the text does not define what x and y are immediately before Eq. (39); the reader has to infer from the sentence that x is penetration volume and y is displacement. Please define both symbols explicitly.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation chain; the author-defined Quality Index is test-calibrated, but this is an evaluation-validity concern rather than a circular step.

full rationale

The model derivation is self-contained. Hand components are learned through codebook losses (Eqs. 1-3), decoded with MANO-based posture and position losses (Eqs. 6-12), and refined by contact and penetration losses (Eqs. 13-17). Deformation is supervised by ground-truth displacement and mesh-smoothness terms (Eqs. 34-38). None of these losses injects the reported result as an input, and the method is evaluated against external baselines on external datasets. The self-citation to the authors' conference paper [24] is descriptive, not load-bearing. The only notable issue is the headline Quality Index (Eq. 39): the weight a=0.301 is calibrated on Obman and GRAB, two of the benchmarks in Table 1, and the 14.1% claim averages across four datasets, including a negative result on Obman. This is a substantive test-set-fitting and reporting concern, but not a circular derivation: Q is an evaluation metric, not a fitted model output or a predicted quantity, and the raw penetration and displacement numbers are reported independently.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claims rest on a modest set of hand-tuned hyperparameters, a newly defined evaluation metric calibrated on a test set, and several dataset and modeling assumptions (MANO, HMDO ground truth, part decomposition, normal-vector penetration as deformation driver). No new physical entities are introduced.

free parameters (4)
  • Loss weights lambda_e, lambda_m, lambda_c, lambda_p, lambda_h, lambda_v = 10, -50, 1500, 5, 0.1, 10
    Chosen by hand in Sec. 5.4; the negative lambda_m is unusual and unexplained.
  • Quality Index weight a = 0.301
    Computed using the method of [51] on Obman and GRAB datasets in Sec. 5.2.1; GRAB is also a test benchmark.
  • Mesh UFormer layer count L and voxel sizes = L=2, V={0, O_size/16, O_size/8}
    Selected by ablation in Sec. 5.6 and fixed in Sec. 5.4.
  • Codebook sizes (number of discrete codes) = not specified
    The paper does not state how many entries each of the seven codebooks contains, which is a key capacity parameter for the VQ-VAE.
assumptions (6)
  • domain assumption MANO parametric hand model [21] is a valid low-dimensional parameterization of hand pose and shape.
    Invoked in Sec. 3.1 as the output representation for all generated hands.
  • domain assumption VQ-VAE discrete latent codes adequately capture the diversity of human grasps.
    The entire architecture is built on this assumption; the paper argues discrete latents are better than continuous ones but does not prove it.
  • domain assumption The HMDO dataset [14] provides accurate ground-truth hand-object deformations for supervision.
    Used to train and evaluate the deformation simulation network in Sec. 5.1 and Sec. 4.3.
  • ad hoc to paper Decomposing the hand into five fingers and a palm is sufficient for grasp generation.
    Motivated in Sec. 1 and Sec. 3.3 by fixed finger positions, but no analysis shows this decomposition is optimal or necessary.
  • ad hoc to paper The scalar Quality Index with weight a=0.301 is a valid measure of grasp quality.
    Proposed in Sec. 5.2.1; the weight is fit to data that includes a test set, and the metric is not validated against human preferences beyond a small 20-participant study.
  • ad hoc to paper Penetration distance computed from normal-vector dot products fully determines the deformation of the object surface.
    The deformation model in Sec. 4.2 uses only this geometric penetration signal as input, ignoring material properties and force distribution.

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Pith. "Pith review of Human Grasp Generation for Rigid and Deformable Objects with Decomposed VQ-VAE." pith.science (2026). https://pith.science/paper/EHAEMMLA

@misc{pith2026250105483,
  author       = {Pith},
  title        = {Pith review of: Human Grasp Generation for Rigid and Deformable Objects with Decomposed VQ-VAE},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EHAEMMLA}},
  note         = {Machine review of arXiv:2501.05483}
}
read the original abstract

Generating realistic human grasps is crucial yet challenging for object manipulation in computer graphics and robotics. Current methods often struggle to generate detailed and realistic grasps with full finger-object interaction, as they typically rely on encoding the entire hand and estimating both posture and position in a single step. Additionally, simulating object deformation during grasp generation is still difficult, as modeling such deformation requires capturing the comprehensive relationship among points of the object's surface. To address these limitations, we propose a novel improved Decomposed Vector-Quantized Variational Autoencoder (DVQ-VAE-2), which decomposes the hand into distinct parts and encodes them separately. This part-aware architecture allows for more precise management of hand-object interactions. Furthermore, we introduce a dual-stage decoding strategy that first predicts the grasp type under skeletal constraints and then identifies the optimal grasp position, enhancing both the realism and adaptability of the model to unseen interactions. Furthermore, we introduce a new Mesh UFormer as the backbone network to extract the hierarchical structural representations from the mesh and propose a new normal vector-guided position encoding to simulate the hand-object deformation. In experiments, our model achieves a relative improvement of approximately 14.1% in grasp quality compared to state-of-the-art methods across four widely used benchmarks. Our comparisons with other backbone networks show relative improvements of 2.23% in Hand-object Contact Distance and 5.86% in Quality Index on deformable and rigid object based datasets, respectively. Our source code and model are available at https://github.com/florasion/D-VQVAE.

Figures

Figures reproduced from arXiv: 2501.05483 by the authors.

Figure 1
Figure 1. Illustration of our proposed grasp generation model. First, we [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 1
Figure 1. Our innovation lies in partitioning the hand into mul [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overall architecture of the proposed DVQ-VAE model, which follows the encoder-decoder paradigm. During training, the model takes hand [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figures from the paper (8 more)
Figure 3
Figure 3. Figure 3: The upper part of the figure illustrates the overall architecture of our DVQ-VAE-2 with Mesh UFormer used for deformation simulation. We [PITH_FULL_IMAGE:figures/full_fig_p005_3.png]
Figure 4
Figure 4. Figure 4: Comparison between our proposed Hand-Object Contact Quan [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Performance comparison of our proposed method with other models in terms of high-quality ratio, concerning the penetration threshold [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Qualitative results comparing our DVQ-VAE-2 with ContactGen [16] and GraspTTA [1] w.r.t rigid objects on the HO-3D dataset [49]. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Diverse grasps generated by our proposed DVQ-VAE-2 for the same rigid object in GRAB dataset [17]. [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Qualitative results by comparing our proposed DVQ-VAE-2 with Mesh UFormer to Deformation Graph [39], PointNet [22], and PointTrans [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: The comparison of deformations simulated by our proposed [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: Comparison of generated grasps with and without our dual [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.