Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization (2203.11471v3)

Abstract: In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute human pose estimation with calibrated camera. Accurate and generalizable absolute 3D human pose estimation from monocular 2D pose input is an ill-posed problem. To address this challenge, we convert the input from pixel space to 3D normalized rays. This conversion makes our approach robust to camera intrinsic parameter changes. To deal with the in-the-wild camera extrinsic parameter variations, Ray3D explicitly takes the camera extrinsic parameters as an input and jointly models the distribution between the 3D pose rays and camera extrinsic parameters. This novel network design is the key to the outstanding generalizability of Ray3D approach. To have a comprehensive understanding of how the camera intrinsic and extrinsic parameter variations affect the accuracy of absolute 3D key-point localization, we conduct in-depth systematic experiments on three single person 3D benchmarks as well as one synthetic benchmark. These experiments demonstrate that our method significantly outperforms existing state-of-the-art models. Our code and the synthetic dataset are available at https://github.com/YxZhxn/Ray3D .

• The paper introduces Ray3D, a novel method that converts 2D keypoint data into normalized 3D ray representations to resolve depth ambiguity.
• It integrates both camera intrinsic and extrinsic parameters into the network, significantly reducing mean per joint position error in experiments.
• Experimental results on real and synthetic datasets demonstrate superior accuracy and generalizability, indicating broad applications in AR and surveillance.

Ray3D: Ray-Based 3D Human Pose Estimation for Monocular Absolute 3D Localization

The paper "Ray3D: Ray-Based 3D Human Pose Estimation for Monocular Absolute 3D Localization" presents an innovative approach to the challenging problem of accurate and generalizable 3D human pose estimation from monocular 2D inputs. The fundamental challenge in monocular absolute 3D human pose estimation is resolving the inherent depth ambiguity, compounded by variations in camera intrinsic and extrinsic parameters. This paper introduces a novel methodology, termed Ray3D, which effectively addresses these challenges.

Methodological Advancements

The key innovation of the Ray3D approach is the conversion of traditional 2D key-point data into 3D normalized ray representations. This transformation significantly mitigates the effects of varying camera intrinsic parameters, such as focal length and principal point shifts, thereby enhancing the model's robustness. Additionally, the method explicitly incorporates camera extrinsic parameters into its network framework. By integrating these parameters, the network not only accounts for varying camera positions and orientations but also leverages this information to solve the ambiguity in human body parts' spatial localization.

Experimental Results

Ray3D's efficacy is thoroughly validated through extensive experimentation on both real and synthetic benchmarks, including three single-person 3D datasets and a purposely constructed synthetic dataset. These benchmarks demonstrate Ray3D's superiority over existing state-of-the-art methods in terms of accuracy and generalizability. Notably, quantitative results show a significant reduction in mean per joint position error (MPJPE) and absolute MPJPE, indicating enhanced spatial accuracy in real-world scenarios.

Implications and Future Directions

The practical application of Ray3D spans various domains such as augmented reality, human-computer interaction, and surveillance, where understanding human positions in absolute world coordinates is crucial. The integration of intrinsic and extrinsic camera parameters into the modeling process may pave the way for more generalized pose estimation models capable of functioning across varying environmental settings with minimal recalibration.

From a theoretical standpoint, the Ray3D framework suggests a shift towards multimodal input representations that leverage camera information directly, which could influence future 3D reconstruction methods. Moreover, the concept of normalized ray representation can be further explored in conjunction with advanced temporal modeling, potentially enhancing performance in dynamic and occluded environments.

Overall, Ray3D represents a promising advancement in the field of 3D pose estimation, combining robust mathematical modeling with practical adaptability. As technology and computational methods continue to evolve, the frameworks proposed in this paper could serve as a basis for future developments in monocular vision-driven applications.