Harvesting Multiple Views for Marker-less 3D Human Pose Annotations

Recent advances with Convolutional Networks (ConvNets) have shifted the bottleneck for many computer vision tasks to annotated data collection. In this paper, we present a geometry-driven approach to automatically collect annotations for human pose prediction tasks. Starting from a generic ConvNet for 2D human pose, and assuming a multi-view setup, we describe an automatic way to collect accurate 3D human pose annotations. We capitalize on constraints offered by the 3D geometry of the camera setup and the 3D structure of the human body to probabilistically combine per view 2D ConvNet predictions into a globally optimal 3D pose. This 3D pose is used as the basis for harvesting annotations. The benefit of the annotations produced automatically with our approach is demonstrated in two challenging settings: (i) fine-tuning a generic ConvNet-based 2D pose predictor to capture the discriminative aspects of a subject's appearance (i.e.,"personalization"), and (ii) training a ConvNet from scratch for single view 3D human pose prediction without leveraging 3D pose groundtruth. The proposed multi-view pose estimator achieves state-of-the-art results on standard benchmarks, demonstrating the effectiveness of our method in exploiting the available multi-view information.Comment: CVPR 2017 Camera Read

Law-determination as grounding: a common grounding framework for jurisprudence

Law being a derivative feature of reality, it exists in virtue of more fundamental things, upon which it depends. This raises the question of what is the relation of dependence that holds between law and its more basic determinants. The primary aim of this paper is to argue that grounding is that relation. We first make a positive case for this claim, and then we defend it from the potential objection that the relevant relation is rather rational determination (Greenberg (2004)). Against this challenge, we argue that the apparent objection is really no objection, for on its best understanding, rational determination turns out to actually be grounding. Finally, we clarify the framework for theories on law-determination that results from embracing our view; by way of illustration, we offer a ground-theoretic interpretation of Hartian positivism, and show how it can defuse an influential challenge to simple positivist accounts of law

On the Benefits of 3D Pose and Tracking for Human Action Recognition

In this work we study the benefits of using tracking and 3D poses for action recognition. To achieve this, we take the Lagrangian view on analysing actions over a trajectory of human motion rather than at a fixed point in space. Taking this stand allows us to use the tracklets of people to predict their actions. In this spirit, first we show the benefits of using 3D pose to infer actions, and study person-person interactions. Subsequently, we propose a Lagrangian Action Recognition model by fusing 3D pose and contextualized appearance over tracklets. To this end, our method achieves state-of-the-art performance on the AVA v2.2 dataset on both pose only settings and on standard benchmark settings. When reasoning about the action using only pose cues, our pose model achieves +10.0 mAP gain over the corresponding state-of-the-art while our fused model has a gain of +2.8 mAP over the best state-of-the-art model. Code and results are available at: https://brjathu.github.io/LARTComment: CVPR2023 (project page: https://brjathu.github.io/LART