In a groundbreaking development, researchers from ETH Zürich and the Max Planck Institute for Intelligent Systems have introduced HOLD, an innovative method designed to tackle the challenge of reconstructing high-quality 3D surfaces of hands and objects from monocular video sequences. This method is applicable in controlled lab settings and real-world egocentric-view videos, and it utilizes interactions between hands and objects to model their shapes and poses jointly.
The evolution of monocular RGB 3D hand reconstruction, building upon Rehg and Kanade’s foundational work, encompasses various approaches. Methods for reconstructing strongly interacting hand poses include biomechanical constraints and spectral graph-based transformers. Some assume object templates in hand-object reconstruction, while others employ temporal models, semi-supervised learning, or contact potential fields. Generalizable methods without object templates use differentiable rendering and data-driven priors. In-hand object scanning focuses on reconstructing canonical 3D object shapes, incorporating hand motion, sequential RGBD images, or volumetric rendering for diverse applications in human-object interactions.
The study tackles the complex task of reconstructing 3D objects and articulated hands from monocular video sequences without relying on pre-scanned object templates or limited training categories. Existing methods often need help with template reliance or restricted generalization capabilities. HOLD, the proposed method, exploits interactions between hands and objects to model their shapes and poses jointly using a compositional neural implicit model. HOLD improves reconstruction quality by incorporating complementary cues from both hands and objects in interactions, showcasing generalization in controlled lab settings and real-world egocentric-view videos.
HOLD is a method for 3D reconstruction of interacting hands and objects from monocular video sequences. HOLD initializes poses, trains HOLD-Net for implicit signed distance fields, and refines poses through interaction constraints. Evaluation of the HO3D-v3 dataset demonstrates accurate 3D geometry reconstruction, with testing across in-the-lab and in-the-wild videos, showcasing robust performance in diverse conditions and perspectives.
The method showcases robust generalization across diverse settings, including static and egocentric-view videos, leveraging hand-object interactions for improved reconstruction quality. Evaluated on the HO3D-v3 dataset with accurate 3D annotations, HOLD achieves precise hand-object geometry by refining poses through interaction constraints and training a compositional implicit signed distance field, contributing to high-quality 3D reconstructions in various environments.
The HOLD method is highly effective in producing top-quality 3D reconstructions of both hand and object surfaces from monocular video sequences, even in challenging real-world scenarios. HOLD surpasses fully-supervised state-of-the-art baselines without relying on 3D hand-object annotation data, thanks to its innovative approach to disentangling and reconstructing 3D hands and objects from 2D observations. The method’s strength is its ability to achieve superior object surface reconstructions compared to isolating objects. While there is potential for improvement through advancements in Structure from Motion and integration of diffusion priors for enhanced object region regularization, the researchers have been transparent about their financial interests and affiliations related to the research project.
Future research directions for HOLD include investigating the integration of detector-free Structure from Motion techniques to enhance robustness and accuracy in challenging in-the-wild scenarios. The exploration of diffusion priors is proposed for a better regularization of object regions, improving object surface reconstruction quality. Additional research avenues involve enhancing the disentanglement and reconstruction of 3D hands and objects from 2D observations, possibly by incorporating constraints or priors. There is also a suggestion to explore the application of HOLD in broader scenarios, such as human-object or object-object interactions, extending the category-agnostic reconstruction approach.
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The post Researchers from ETH Zürich and Max Planck Introduce ‘HOLD’: A Groundbreaking Category-Agnostic AI Method for 3D Hand-Object Reconstruction from Monocular Videos appeared first on MarkTechPost.
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