Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time (2106.05266v1)

Abstract: Estimating 3D hand and object pose from a single image is an extremely challenging problem: hands and objects are often self-occluded during interactions, and the 3D annotations are scarce as even humans cannot directly label the ground-truths from a single image perfectly. To tackle these challenges, we propose a unified framework for estimating the 3D hand and object poses with semi-supervised learning. We build a joint learning framework where we perform explicit contextual reasoning between hand and object representations by a Transformer. Going beyond limited 3D annotations in a single image, we leverage the spatial-temporal consistency in large-scale hand-object videos as a constraint for generating pseudo labels in semi-supervised learning. Our method not only improves hand pose estimation in challenging real-world dataset, but also substantially improve the object pose which has fewer ground-truths per instance. By training with large-scale diverse videos, our model also generalizes better across multiple out-of-domain datasets. Project page and code: https://stevenlsw.github.io/Semi-Hand-Object

• The paper introduces a semi-supervised framework that jointly estimates 3D hand and object poses using a Transformer-based contextual reasoning module.
• It leverages spatial-temporal constraints to generate robust pseudo-labels, significantly improving hand mesh accuracy and object detection under occlusion.
• Experimental results on benchmarks like HO-3D demonstrate reduced errors and higher F-scores, setting a new state-of-the-art for hand-object pose estimation.

Semi-Supervised 3D Hand-Object Pose Estimation with Interactions in Time

The paper presents a novel semi-supervised framework for estimating 3D hand and object poses in images, addressing inherent challenges due to occlusions and limited annotations. The core motivation stems from the necessity to understand hand-object interactions, crucial in domains such as augmented reality and human-computer interaction. The main contribution is twofold: a joint learning framework incorporating a Transformer-based contextual reasoning module and a semi-supervised learning pipeline leveraging spatial-temporal constraints for pseudo-label generation.

The proposed method combines explicit modeling of spatial and temporal continuity within large-scale video datasets to generate pseudo-labels for hand poses. This design not only enhances the hand pose estimation but also indirectly improves object pose estimations via the interaction modeling facilitated by the Transformer. The approach is structured to capture the contextual relationship between hand and object through co-learned features, which are refined via separate decoding structures for both components.

Key components of the model include a shared encoder based on ResNet-50, enhanced by Feature Pyramid Networks (FPN) and the Transformer module for contextual reasoning, where hand-object correlations are explicitly modeled. The method's novelty lies in leveraging both spatial-temporal consistency across video frames and the Transformer’s attention mechanism to improve interaction-based pose estimation.

Quantitative results highlight significant improvements over state-of-the-art approaches. On the HO-3D benchmark, the model achieves a noticeable reduction in hand mesh errors and an increase in F-scores, demonstrating superior generalization across challenging datasets such as FPHA and FreiHand. Object pose estimation also benefits from this framework, particularly for objects under occlusion, as indicated by more than a 10% average improvement in the ADD-0.1D metric across different objects after employing the pseudo-labels.

The ablation studies presented further validate the Transformer’s contextual reasoning role and the importance of spatial-temporal constraints in pseudo-label filtering. Comparisons reveal that enriching object queries using hand-object interaction features significantly boosts performance, setting a new baseline for hand-object pose estimation tasks.

Given these advancements, the implications for practical applications and theoretical understanding in 3D vision and interaction reasoning are expansive. Practically, this method can be integrated into interactive systems for improved real-time augmented interface experiences. Theoretically, it presents an approach for handling semi-supervised learning in environments with insufficient ground truth data, highlighting the potential of video data as a robust resource for enhancing model performance.

Future research directions may focus on extending this architecture to fully unsupervised paradigms or integrating it into real-time processing systems. Further explorations could involve refining Transformer architectures to enhance interaction modeling efficiency over varying scale datasets or environments, potentially broadening its application to more diverse scenarios, such as robotic manipulation and immersive virtual environments.

Overall, this work signifies an important step in accurately capturing the complex dynamics inherent in hand-object interactions, utilizing advanced machine learning techniques to navigate the limitations of annotated data scarcity.