OmniHands: Towards Robust 4D Hand Mesh Recovery via A Versatile Transformer (2405.20330v3)

Abstract: In this paper, we introduce OmniHands, a universal approach to recovering interactive hand meshes and their relative movement from monocular or multi-view inputs. Our approach addresses two major limitations of previous methods: lacking a unified solution for handling various hand image inputs and neglecting the positional relationship of two hands within images. To overcome these challenges, we develop a universal architecture with novel tokenization and contextual feature fusion strategies, capable of adapting to a variety of tasks. Specifically, we propose a Relation-aware Two-Hand Tokenization (RAT) method to embed positional relation information into the hand tokens. In this way, our network can handle both single-hand and two-hand inputs and explicitly leverage relative hand positions, facilitating the reconstruction of intricate hand interactions in real-world scenarios. As such tokenization indicates the relative relationship of two hands, it also supports more effective feature fusion. To this end, we further develop a 4D Interaction Reasoning (FIR) module to fuse hand tokens in 4D with attention and decode them into 3D hand meshes and relative temporal movements. The efficacy of our approach is validated on several benchmark datasets. The results on in-the-wild videos and real-world scenarios demonstrate the superior performances of our approach for interactive hand reconstruction. More video results can be found on the project page: https://OmniHand.github.io.

• The paper introduces a unified transformer approach integrating a Relation-aware Two-Hand Tokenization (RAT) and a 4D Interaction Reasoning (FIR) module for robust 4D hand mesh recovery.
• It leverages a novel tokenization strategy that embeds spatial relationship cues between hand images to enhance joint reasoning over inter-hand dynamics.
• Evaluations demonstrate significant improvements in mesh accuracy and temporal consistency, reducing per-vertex errors in interactive AR/VR scenarios.

Overview

The OmniHands framework proposes a unified framework for robust 4D hand mesh recovery that can operate on both monocular and multi-view inputs. Its core innovation lies in addressing the limitations of previous methods by introducing two key modules: the Relation-aware Two-Hand Tokenization (RAT) strategy and the 4D Interaction Reasoning (FIR) module. This design enables the architecture to concurrently model single-hand inputs and complex inter-hand positional relationships within the same pipeline, which is critical for real-world interactive scenarios.

Architecture and Tokenization

The architecture is leveraged by a transformer-based backbone that processes tokenized representations of hand images. The RAT module embeds explicit positional relationship information between two hands. Unlike conventional tokenization techniques that process hand regions in isolation, RAT enriches each token with spatial relationship cues. Concretely, let X and Y denote the feature maps extracted from hand images, then the tokenization function includes a learnable positional encoding P as follows:

$T_i = f_{\text{RAT}}(X_i, Y_i, P_i)$

This formulation facilitates joint reasoning over both hands, contributing to richer inter-hand representations. The RAT module allows the network to benefit from attention mechanisms that not only fuse features across spatial dimensions but also capture relative dynamics between interacting hands.

Temporal and Interaction Reasoning

The 4D Interaction Reasoning (FIR) module extends the spatial context to the temporal domain by processing a sequence of hand token features across frames. The transformer’s attention mechanism is utilized here to integrate long-term temporal dependencies as well as instantaneous spatial relationships. By applying multi-head attention over concatenated tokens from successive frames, the FIR module can decode aggregated features into 3D hand meshes with accurate temporal coherence:

$$\text{Mesh}_t = g_{\text{FIR}}(T_{t-\Delta t}, \ldots, T_t, \ldots, T_{t+\Delta t})$$

This design ensures that the reconstructed 3D meshes capture nuanced hand dynamics, which is particularly useful in interactive scenarios where the timing and sequence of hand poses are critical.

Performance and Quantitative Evaluation

One of the critical contributions of OmniHands is its ability to surpass previous state-of-the-art methods on challenging benchmarks. The paper reports significant improvements in terms of mesh reconstruction accuracy and temporal consistency metrics. In cases of interactive hand scenarios, the method demonstrates lower mean per-vertex error and improved temporal smoothness metrics compared to previous approaches lacking unified modeling. Although the exact numerical improvements are provided in the paper, the results indicate an enhancement of several millimeters in vertex localization error over prior baselines, which is critical in applications such as AR/VR and human-computer interaction.

Practical Implementation Considerations

Implementing OmniHands in a real-world system involves several practical considerations:

• Computational Requirements:

The transformer-based design, especially when processing high-resolution inputs over multiple frames, demands significant computational resources. Efficient GPU utilization and potential model quantization or pruning techniques may be utilized to deploy the model in real-time systems.

• Data Preprocessing:

A robust pre-processing pipeline is required to form consistent input representations from monocular and multi-view setups. Calibration of multi-camera systems and normalization of hand image regions are crucial steps to ensure optimal performance.

• Model Training:

The training regime should include diverse datasets that capture a wide range of hand poses and interactions. A mixed scheme combining supervised loss on known ground-truth meshes along with temporal consistency constraints can be particularly effective. Loss functions such as the per-vertex L2 loss and temporal smoothness loss can ensure robust convergence.

• Integration into Systems:

For interactive applications, latency is a key bottleneck. Deploying the model using frameworks such as TensorRT for inference acceleration or implementing a multi-threaded pipeline to overlap pre-processing and inference can help achieve lower latency. Additionally, considering a modular architecture can facilitate integration into larger systems such as AR/VR platforms or robotics, where interactive hand feedback is required.

Pseudocode and Implementation Sketch

Below is a high-level pseudocode snippet demonstrating the forward pass of the OmniHands framework:

def forward_pass(frame_sequence, hand_regions): # Extract feature maps for each hand region per frame features = [extract_features(frame, hand_regions) for frame in frame_sequence] # Tokenization using Relation-aware Two-Hand Tokenization (RAT) tokens_sequence = [] for features_frame in features: tokens = relation_aware_tokenization(features_frame, positional_encodings) tokens_sequence.append(tokens) # Temporal fusion using 4D Interaction Reasoning (FIR) module fused_tokens = fir_module(tokens_sequence) # Decode fused tokens to reconstruct 3D hand meshes and temporal dynamics hand_meshes = mesh_decoder(fused_tokens) return hand_meshes def relation_aware_tokenization(features, pos_encodings): # Embedding the features with spatial relationship encoding tokens = transformer_tokenizer(features, pos_encodings) return tokens def fir_module(tokens_sequence): # Using multi-head attention to fuse tokens across the time dimension fused = temporal_transformer(tokens_sequence) return fused

Concluding Remarks

OmniHands introduces a robust solution for interactive 4D hand mesh recovery that is flexible across varying input modalities. By integrating a Relation-aware Tokenization approach with an advanced FIR module, the architecture addresses the joint spatial-temporal modeling challenges inherent in interactive hand scenarios. The reported improvements in mesh accuracy and temporal consistency, along with its applicability to diverse deployment scenarios, make this approach a significant step forward for real-world hand pose estimation tasks.