Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark (1804.01984v1)

Abstract: Human parsing and pose estimation have recently received considerable interest due to their substantial application potentials. However, the existing datasets have limited numbers of images and annotations and lack a variety of human appearances and coverage of challenging cases in unconstrained environments. In this paper, we introduce a new benchmark named "Look into Person (LIP)" that provides a significant advancement in terms of scalability, diversity, and difficulty, which are crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels and 16 body joints, which are captured from a broad range of viewpoints, occlusions, and background complexities. Using these rich annotations, we perform detailed analyses of the leading human parsing and pose estimation approaches, thereby obtaining insights into the successes and failures of these methods. To further explore and take advantage of the semantic correlation of these two tasks, we propose a novel joint human parsing and pose estimation network to explore efficient context modeling, which can simultaneously predict parsing and pose with extremely high quality. Furthermore, we simplify the network to solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into the parsing results without resorting to extra supervision. The dataset, code and models are available at http://www.sysu-hcp.net/lip/.

• The paper presents the LIP dataset with over 50,000 images annotated with 19 semantic parts and 16 body joints, setting a new standard for human-centric analysis.
• The paper introduces a unified Joint Parsing and Pose Estimation Network (JPPNet) that employs multi-scale feature extraction and iterative refinement for superior accuracy.
• The paper proposes a self-supervised strategy (SS-JPPNet) that infers joint structures from parsing annotations, enhancing alignment with human body configurations.

Insightful Overview of the Paper: "Look into Person: Joint Body Parsing and Pose Estimation Network and A New Benchmark"

This paper introduces a comprehensive dataset labeled "Look into Person (LIP)" aimed at advancing human-centric analysis tasks, specifically human parsing and pose estimation. LIP addresses limitations in existing datasets by significantly amplifying the volume, diversity, and complexity of annotated images. This dataset includes over 50,000 images, each annotated with 19 semantic part labels and 16 body joints, providing a diverse range of scenarios that include multiple viewpoints, occlusions, and complex backgrounds. The dataset sets a new standard for benchmarks in human parsing and pose estimation research by introducing a publicly available evaluation server.

Core Contributions

1. Benchmark Dataset: The LIP dataset is presented as a substantial improvement over previous human parsing and pose estimation datasets in terms of scale and diversity. This dataset introduces a comprehensive set of human parsing and pose annotations, enabling detailed analysis and pushing towards holistic human understanding in computer vision.
2. Joint Parsing and Pose Estimation Model: The paper proposes a unified network, the Joint Parsing and Pose Estimation Network (JPPNet), which is designed to perform human parsing and pose estimation simultaneously. This network is structured to iterate through coarse-to-fine schemes using multi-scale feature extraction and iterative refinement, creating high-quality output for both tasks.
3. Self-supervised Learning Strategy: To accommodate datasets that lack pose annotations, the paper introduces a self-supervised approach named SS-JPPNet. This system generates joint structures directly from parsing annotations, offering a structure-sensitive learning process that aligns parsing results with human joint structures.

Empirical Analysis and Results

The empirical evaluation demonstrates JPPNet’s superiority over existing models, notably improving mean Intersection over Union (IoU) scores in human parsing tasks while offering enhanced accuracy for pose estimation. The joint framework, when trained and tested on the challenging LIP dataset, consistently outperforms existing state-of-the-art methods in both human parsing and pose estimation. Through extensive analysis on different factors such as occlusion, viewpoint, and scenario diversity, the paper successfully illustrates the robustness and efficacy of the developed dataset and models.

Implications and Future Directions

The introduction of the LIP dataset and the JPPNet model have significant implications for practical applications in domains like surveillance, human-computer interaction, and augmented reality. The dataset sets a new benchmark for evaluating and developing robust models capable of handling complex, real-world scenarios. Future research can build on these contributions by exploring how the joint architecture can be further integrated with other interdisciplinary tasks in AI, leveraging the semantic contextual information provided by the dataset.

Additional studies may investigate improvements in model efficiency, scalability, and generalizability across diverse data sources. The potential inclusion of real-time inferencing capabilities would advance applications in interactive environments. Furthermore, expanding the dataset to include annotations for other dynamic human states and interactions could broaden the dataset’s applicability and push the frontier in holistic scene understanding.

Overall, this work significantly enhances the resources available to the computer vision community aiming to tackle complex human-centric tasks, laying the groundwork for future innovations and applications.