MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network (2310.16288v1)

Abstract: Recent transformer-based approaches have demonstrated excellent performance in 3D human pose estimation. However, they have a holistic view and by encoding global relationships between all the joints, they do not capture the local dependencies precisely. In this paper, we present a novel Attention-GCNFormer (AGFormer) block that divides the number of channels by using two parallel transformer and GCNFormer streams. Our proposed GCNFormer module exploits the local relationship between adjacent joints, outputting a new representation that is complementary to the transformer output. By fusing these two representation in an adaptive way, AGFormer exhibits the ability to better learn the underlying 3D structure. By stacking multiple AGFormer blocks, we propose MotionAGFormer in four different variants, which can be chosen based on the speed-accuracy trade-off. We evaluate our model on two popular benchmark datasets: Human3.6M and MPI-INF-3DHP. MotionAGFormer-B achieves state-of-the-art results, with P1 errors of 38.4mm and 16.2mm, respectively. Remarkably, it uses a quarter of the parameters and is three times more computationally efficient than the previous leading model on Human3.6M dataset. Code and models are available at https://github.com/TaatiTeam/MotionAGFormer.

Enhancing 3D Human Pose Estimation with MotionAGFormer

The paper "MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network" presents a hybrid architecture designed to tackle the challenges associated with 3D human pose estimation from 2D sequences. Drawing inspiration from existing transformer and graph-based models, the research introduces MotionAGFormer, a model that synergistically combines transformers and graph convolutional networks to offer a more comprehensive view of human motion.

Architectural Innovation

At the core of this paper is the Attention-GCNFormer (AGFormer) block, which interleaves Transformer and Graph Convolutional Network (GCN) streams to capture both global and local dependencies, respectively. While Transformers are adept at encoding long-range dependencies across joints, their performance in modeling the localized, intricate relationships typical of human motion poses limitations. The GCNFormer module addresses this gap by focusing on local relationships between adjacent joints. Through adaptive fusion, both streams within the AGFormer block produce a balanced representation, adept in learning the underlying structure of 3D human poses.

The architecture builds on a linear sequence of these AGFormer blocks, resulting in four model variants—MotionAGFormer-XS, -S, -B, and -L—that cater to a range of speed-accuracy trade-offs required for different applications.

Empirical Results

The MotionAGFormer-B, touted as the base version, manages to achieve state-of-the-art accuracy with an MPJPE P1 error at 38.4 mm on the Human3.6M dataset, utilizing only a quarter of the parameters of prior leading models and a notably efficient computation process. Importantly, the proposed hybrid model maintains its accuracy even under noisy input conditions, as indicated by its results on ground truth 2D data. These findings affirm the effectiveness of coupling global and local data processing mechanisms within a unified architecture.

Evaluation on the MPI-INF-3DHP dataset further demonstrates the MotionAGFormer models' capacity to deliver low P1 errors while maintaining competitive PCK and AUC metrics, highlighting their adaptability across varied datasets and conditions.

Implications and Future Directions

MotionAGFormer introduces an efficient and scalable alternative for 3D human pose estimation, offering multiple configurations to leverage computational resources optimally based on application needs. The impressive computational efficiency ensures its relevance across real-time and resource-constrained environments, broadening its application scope in fields like augmented and virtual reality, human-computer interaction, and autonomous systems.

The adaptive fusion of representations hints at further explorations in refining joint weighting mechanisms, potentially fostering the development of more nuanced understanding of motion by integrating other modalities such as texture or environmental data.

This work provides a solid foundation for future models aiming to enhance 3D human pose estimation via hybrid structural designs. The adaptability and robustness demonstrated by MotionAGFormer have significant implications for the expansion of AI-driven motion capture technologies beyond controlled settings, contributing to more immersive and interactive digital experiences. Future research could explore optimizing temporal aspects of GCNFormer further, investigating the integration of dynamic scene understanding, and addressing challenges in occlusion or overlapping poses commonly encountered in complex scenes.