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ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis
Kailin Li, Lixin Yang, Xinyu Zhan, Jun Lv, Wenqiang Xu, Jiefeng Li, Cewu Lu
TL;DR
Real-world datasets lack diverse hand poses, object types, and viewpoints, while constructing valid, diverse interactions and using synthetic data efficiently remain challenging. ArtiBoost samples and synthesizes hand-object-viewpoint triplets, reweighting hard items through training feedback; even a simple baseline outperforms previous state-of-the-art methods on popular datasets.
Problem
Real-world datasets lack diverse hand poses, object types, and viewpoints, and synthesizing valid, diverse interactions while efficiently learning from synthetic data remains challenging.
Source: §1 · pp. 1–2Method
ArtiBoost explores a discrete space of object types, interacting hand poses, and viewpoints, synthesizes contact-guided grasps, and uses training-loss feedback to reweight hard triplets.
Source: §1 · p. 2, §3.1 · pp. 3–4Results
Even with a simple baseline, ArtiBoost outperforms previous state-of-the-art methods on popular datasets.
Source: §5 · p. 8Takeaways & Limitations
The authors present CCV-space as a step toward generic articulated pose estimation, which they leave for future work.
Source: §5 · p. 8ArtiBoost does not explicitly mitigate the synthetic-real domain gap, and its non-differentiable renderer limits exploration to predefined lookup tables such as CCV-space.
Source: §5 · p. 8
Abstract
from arXiv · showhide
Estimating the articulated 3D hand-object pose from a single RGB image is a highly ambiguous and challenging problem, requiring large-scale datasets that contain diverse hand poses, object types, and camera viewpoints. Most real-world datasets lack these diversities. In contrast, data synthesis can easily ensure those diversities separately. However, constructing both valid and diverse hand-object interactions and efficiently learning from the vast synthetic data is still challenging. To address the above issues, we propose ArtiBoost, a lightweight online data enhancement method. ArtiBoost can cover diverse hand-object poses and camera viewpoints through sampling in a Composited hand-object Configuration and Viewpoint space (CCV-space) and can adaptively enrich the current hard-discernable items by loss-feedback and sample re-weighting. ArtiBoost alternatively performs data exploration and synthesis within a learning pipeline, and those synthetic data are blended into real-world source data for training. We apply ArtiBoost on a simple learning baseline network and witness the performance boost on several hand-object benchmarks. Our models and code are available at https://github.com/lixiny/ArtiBoost.
1. Introduction
Real-world data and offline synthesis have difficulty providing diverse, useful hand-object interactions. ArtiBoost explores and synthesizes hand-object-viewpoint combinations, then uses training feedback to adapt which combinations it samples.
1. Introduction
- The paper focuses on hand-object pose estimation, which the authors identify as relevant to understanding human behavior.Pose estimation for articulated bodies can also benefit downstream robotics and augmented-reality tasks.Source: §1 · p. 1
- Real-world recording limits pose diversity, while conventional synthesis struggles to produce valid, diverse interactions and can waste effort on redundant or easily discernable samples.The paper identifies offline synthesis without model feedback as inefficient for learning.Source: §1 · pp. 1–2
- ArtiBoost represents object type, hand pose, and viewpoint in CCV-space and uses contact-guided grasp synthesis to construct valid, diverse interactions.The grasp synthesis fits MANO hands to objects using contact constraints.Source: §1 · p. 2
- Training losses guide reweighting of hard-discernable hand-object-viewpoint triplets, which are rendered and mixed with real images for HOPE training.The sampling adapts to the current model during training.Source: §1 · p. 2
2. Related Work
Prior work addresses hand-object pose estimation through several prediction paradigms and synthesizes grasps through manual, metric-based, or data-driven approaches. ArtiBoost combines contact-guided synthesis with adaptive hard-example reweighting and can be integrated into HOPE frameworks through the data loader.
2. Related Work
- HOPE methods include unified hand-object prediction, contact modeling, and learning-free aggregation of visual cues; the paper uses classification and regression baselines.The cited learning-free methods combine object detection, hand pose estimation, and instance segmentation.Source: §2 · p. 2
- ArtiBoost can be integrated into an arbitrary HOPE framework by modifying its current data loader.Source: Figure 2 · p. 3
- Hand-object grasp synthesis includes manual labeling, grasp-metric simulators, and data-driven conditional VAEs.ArtiBoost presents an automatic contact-guided optimization method for constructing interaction poses.Source: §2 · pp. 2–3
- Hard-example mining uses prediction error to prioritize underlearned samples; ArtiBoost adapts this idea by reweighting hard-discernable hand-object-viewpoint triplets.The passage contrasts this simple reweighting strategy with adversarial methods that require effort to ensure sample validity.Source: §2 · p. 3
3. Method
ArtiBoost alternates exploration of hand-object-viewpoint configurations with online synthesis, mixing rendered samples with real images and using training losses to reweight future sampling. Its method defines valid interaction configurations, renders varied synthetic examples, and trains HOPE models with complementary losses.
3. Method
- ArtiBoost samples hand-object-viewpoint triplets, renders them as synthetic images, mixes them with real source images, and reweights sampling after each training epoch using losses.The training loop samples triplets from a weighted CCV-space map and feeds the mixed batches to the HOPE framework.Source: §3 · p. 3
3.1. Online Exploration in CCV-Space
- CCV-space organizes scenarios by object type, interacting hand pose, and viewpoint; contact-guided fitting constructs diverse hand-object configurations within valid hand-pose constraints.The hand-pose space uses 21 degrees of freedom, while contact-based fitting attracts unattached fingertips to contact points and pushes intersecting anchors outward.Source: §3.1 · pp. 3–4
- Sampling weights favor hard-to-discern triplets, with each epoch’s sample MPJPE percentile determining a reciprocal weight update.The method increases weights for difficult samples and decreases weights for samples that are already easy to discern.Source: §3.1 · p. 5
3.2. Online Synthesis for HOPE task
- Synthetic images combine sampled triplets with backgrounds, randomized hand and viewpoint disturbances, and hand textures before training alongside real images.Hand-pose disturbances are processed with GrabNet’s RefineNet to mitigate inter-penetration.Source: §3 · p. 3, §3.2 · pp. 5–6
3.3. Learning Framework
- Clas and Reg use ResNet-34 backbones and train with a four-term loss covering joint locations, object rotation, ordinal depth relations, and object symmetry.Clas estimates joint locations from 3D heatmaps, whereas Reg regresses MANO parameters and the object centroid; both predict object rotation.Source: §3.3 · p. 6
4. Experiment and Result
ArtiBoost improves hand-object pose estimation across reported benchmarks, and its ablations support CCV-space synthesis and online sample re-weighting. The experiments also test reduced real-data training and transfer to another HOPE framework.
4.1. Dataset and Metrics
- Evaluation covers FHAB, HO3D, and DexYCB, reporting wrist-aligned MPJPE for hands and MPCPE or symmetry-aware MSSD for objects.The symmetry model uses the MSSD metric and follows the BOP challenge protocol.Source: §4.1 · pp. 6–7
4.2. HOPE Network Performance
- HO3D and DexYCB visualizations pair projected hand joints and object corners with hand-object geometry rendered from camera and alternate views.A pretrained inverse-kinematics network maps hand joints to hand mesh surfaces for visualization.Source: §4.2 · p. 7, Figure 7 · p. 7
- 28% and 10% MPJPE improvement over previous SOTA and, respectively, is reported for ArtiBoost on HO3D.The comparison removes the unseen pitcher base when calculating MPCPE.Source: §4.2 · p. 7, Table 2 · p. 7
- With ArtiBoost, Clas outperforms the recent Transformer-based method [23], and results on DexYCB and HO3D v3 also demonstrate effectiveness.The cited tables report the symmetry-aware HO3D comparison, DexYCB results, and HO3D v3 results.Source: §4.2 · p. 7, Table 3 · p. 7, Table 4 · p. 7
4.3. Ablation Study
- Online sample re-weighting converges faster and achieves a higher score than fixed-weight sampling, while CCV-space poses outperform conventional synthetic poses.The conventional-pose comparison uses YCBAfford; the online-versus-offline experiments use the DexYCB testing set.Source: §4.3 · pp. 7–8, Table 6 · p. 8, Figure 8 · p. 8
4.4. Applications
- Training with 10% of HO3D real data plus 100% N of ArtiBoost synthetic poses can outperform training with 100% real-world data.Porting ArtiBoost to Hasson et al.'s camera-space HOPE model significantly improves all reported metrics.Source: §4.4 · p. 8, Table 7 · p. 8, Table 8 · p. 8
5. Discussion
ArtiBoost enriches articulated pose estimation through exploration and synthesis, and the paper reports that a simple baseline surpasses prior state of the art on popular datasets. The method does not explicitly mitigate the synthetic-real domain gap, and its current exploration is limited to predefined lookup tables.
Limitations
- ArtiBoost does not explicitly mitigate the synthetic-real domain gap, because the authors found pose diversity, rather than more realistic appearance, to dominate improvement.This is the authors’ stated finding and does not claim that the domain gap is absent.Source: §5 · p. 8
- Because its renderer is non-differentiable, current ArtiBoost supports exploration only in a predefined lookup table such as CCV-space.The authors propose investigating generative and contrastive models for features shared by real and synthetic images.Source: §5 · p. 8
Conclusion
- The paper reports that ArtiBoost boosts a simple baseline beyond prior SOTA on popular datasets and identifies generic articulated pose estimation as future work.The conclusion presents CCV-space as opening a path toward that broader task.Source: §5 · p. 8
A. The Training Details
The experiments use ImageNet-initialized classification and regression baselines, Adam training, and approximately equal batches of real and ArtiBoost synthetic data.
The Training Details
- Classification and regression baselines use ImageNet-pretrained backbones; the classification model outputs 28 × 28 × 28 3D heatmaps and predicts object rotation through an MLP.The rotation branch uses three hidden layers of 512, 256, and 128 neurons, followed by a six-neuron continuity-representation output.Source: A. The Training Details · p. 12
- Training runs for 100 epochs with Adam at a learning rate of 5 × 10^-5 and a total batch size of 128 across two GPUs.Source: A. The Training Details · p. 12
- Training batches blend original real-world data and ArtiBoost synthetic data at an approximately 1:1 rate, which the authors report empirically gave the best performance.Source: A. The Training Details · p. 12
B. Objects’ Symmetry Axes
Because hand occlusion makes object pose prediction more challenging in hand-object datasets, the method relaxes restrictions on object symmetry axes and estimates valid rotations using alignment and ICP refinement.
B. Objects’ Symmetry Axes
- Symmetry-axis restrictions are relaxed because hand occlusion makes object pose prediction harder than in object-only datasets, following [23].Source: B. Objects’ Symmetry Axes · p. 12
- Table 9 lists YCB objects’ symmetry axes; ∞ denotes an object that is revolutionary by that axis.Source: Table 9 · p. 12
- ICP refines each predefined symmetry rotation against the object-surface point cloud, yielding a rotation in the valid set S.The method aligns the object to its principal axis of inertia, defines a symmetry axis and angle, then fits a correction with ICP.Source: B. Objects’ Symmetry Axes · p. 12
- Figure 9 marks the objects’ principal inertia axes with x in red, y in green, and z in blue.Source: Figure 9 · p. 12
C. Additional Results
On DexYCB, ArtiBoost yields more accurate predictions for almost every YCB object; additional qualitative results are shown for DexYCB and HO3D.
C. Additional Results
- ArtiBoost produces more accurate pose predictions for almost every YCB object on DexYCB, with full MSSD results reported in Table 10.Source: C. Additional Results · p. 12, Table 10 · p. 13
- Figure 10 shows additional qualitative results, with HO3D examples in rows 1–3 and DexYCB examples in rows 4–8.Source: Figure 10 · p. 13