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Social dynamics in close human interactions pose significant challenges for Human Mesh Estimation (HME), particularly due to the complexity of physical contacts and the scarcity of training data. Addressing these challenges, we introduce a novel data generation method that utilizes Large Vision Language Models (LVLMs) to annotate contact maps which guide test-time optimization to produce paired image and pseudo-ground truth meshes. This methodology not only alleviates the annotation burden but also enables the assembly of a comprehensive dataset specifically tailored for close interactions in HME. Our Ask Pose Unite (APU) dataset, comprising over 6.2k human mesh pairs in contact covering diverse interaction types, is curated from images depicting naturalistic person-to-person scenes. We empirically show that using our dataset to train a diffusion-based contact prior, used as guidance during optimization, improves mesh estimation on unseen interactions. Our work addresses longstanding challenges of data scarcity for close interactions in HME enhancing the field's capabilities of handling complex interaction scenarios., Comment: Project webpage: https://laubravo.github.io/apu_website/

3D object detection at long range is crucial for ensuring the safety and efficiency of self driving vehicles, allowing them to accurately perceive and react to objects, obstacles, and potential hazards from a distance. But most current state of the art LiDAR based methods are range limited due to sparsity at long range, which generates a form of domain gap between points closer to and farther away from the ego vehicle. Another related problem is the label imbalance for faraway objects, which inhibits the performance of Deep Neural Networks at long range. To address the above limitations, we investigate two ways to improve long range performance of current LiDAR based 3D detectors. First, we combine two 3D detection networks, referred to as range experts, one specializing at near to mid range objects, and one at long range 3D detection. To train a detector at long range under a scarce label regime, we further weigh the loss according to the labelled point's distance from ego vehicle. Second, we augment LiDAR scans with virtual points generated using Multimodal Virtual Points (MVP), a readily available image-based depth completion algorithm. Our experiments on the long range Argoverse2 (AV2) dataset indicate that MVP is more effective in improving long range performance, while maintaining a straightforward implementation. On the other hand, the range experts offer a computationally efficient and simpler alternative, avoiding dependency on image-based segmentation networks and perfect camera-LiDAR calibration., Comment: Accepted to Intelligent Vehicle Symposium (IV) 2024

Crop classification via deep learning on ground imagery can deliver timely and accurate crop-specific information to various stakeholders. Dedicated ground-based image acquisition exercises can help to collect data in data scarce regions, improve control on timing of collection, or when study areas are to small to monitor via satellite. Automatic labelling is essential when collecting large volumes of data. One such data collection is the EU's Land Use Cover Area frame Survey (LUCAS), and in particular, the recently published LUCAS Cover photos database. The aim of this paper is to select and publish a subset of LUCAS Cover photos for 12 mature major crops across the EU, to deploy, benchmark, and identify the best configuration of Mobile-net for the classification task, to showcase the possibility of using entropy-based metrics for post-processing of results, and finally to show the applications and limitations of the model in a practical and policy relevant context. In particular, the usefulness of automatically identifying crops on geo-tagged photos is illustrated in the context of the EU's Common Agricultural Policy. The work has produced a dataset of 169,460 images of mature crops for the 12 classes, out of which 15,876 were manually selected as representing a clean sample without any foreign objects or unfavorable conditions. The best performing model achieved a Macro F1 (M-F1) of 0.75 on an imbalanced test dataset of 8,642 photos. Using metrics from information theory, namely - the Equivalence Reference Probability, resulted in achieving an increase of 6%. The most unfavorable conditions for taking such images, across all crop classes, were found to be too early or late in the season. The proposed methodology shows the possibility for using minimal auxiliary data, outside the images themselves, in order to achieve a M-F1 of 0.817 for labelling between 12 major European crops.

Higgs boson pair production is traditionally considered to be of particular interest for a measurement of the trilinear Higgs self-coupling. Yet it can offer insights into other couplings as well, since - in an effective field theory (EFT) parameterisation of potential new physics - both the production cross section and kinematical properties of the Higgs boson pair depend on various other Wilson coefficients of EFT operators. This note summarises the ongoing efforts related to the development of EFT tools for Higgs boson pair production in gluon fusion, and provides recommendations for the use of distinct EFT parameterisations in the Higgs boson pair production process. This document also outlines where further efforts are needed and provides a detailed analysis of theoretical uncertainties. Additionally, benchmark scenarios are updated. We also re-derive a parameterisation of the next-to-leading order (NLO) QCD corrections in terms of the EFT Wilson coefficients both for the total cross section and the distribution in the invariant mass of the Higgs boson pair, providing for the first time also the covariance matrix. A reweighting procedure making use of the newly derived coefficients is validated, which can be used to significantly speed up experimental analyses., Comment: Matches CDS record LHCHWG-2022-004; ancillary file contains newly derived coefficients for reweighting procedure. Update contains revised figures and new references

Recent text-to-image generative models have exhibited remarkable abilities in generating high-fidelity and photo-realistic images. However, despite the visually impressive results, these models often struggle to preserve plausible human structure in the generations. Due to this reason, while generative models have shown promising results in aiding downstream image recognition tasks by generating large volumes of synthetic data, they are not suitable for improving downstream human pose perception and understanding. In this work, we propose a Diffusion model with Human Pose Correction (Diffusion-HPC), a text-conditioned method that generates photo-realistic images with plausible posed humans by injecting prior knowledge about human body structure. Our generated images are accompanied by 3D meshes that serve as ground truths for improving Human Mesh Recovery tasks, where a shortage of 3D training data has long been an issue. Furthermore, we show that Diffusion-HPC effectively improves the realism of human generations under varying conditioning strategies.

Approximate distance estimation can be used to determine fundamental landscape properties including complexity and openness. We show that variations in the skyline of landscape photos can be used to estimate distances to trees on the horizon. A methodology based on the variations of the skyline has been developed and used to investigate potential relationships with the distance to skyline objects. The skyline signal, defined by the skyline height expressed in pixels, was extracted for several Land Use/Cover Area frame Survey (LUCAS) landscape photos. Photos were semantically segmented with DeepLabV3+ trained with the Common Objects in Context (COCO) dataset. This provided pixel-level classification of the objects forming the skyline. A Conditional Random Fields (CRF) algorithm was also applied to increase the details of the skyline signal. Three metrics, able to capture the skyline signal variations, were then considered for the analysis. These metrics shows a functional relationship with distance for the class of trees, whose contours have a fractal nature. In particular, regression analysis was performed against 475 ortho-photo based distance measurements, and, in the best case, a R2 score equal to 0.47 was achieved. This is an encouraging result which shows the potential of skyline variation metrics for inferring distance related information.

Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining the use of cheap high quality cameras with recent advances in deep learning compute solutions to derive relevant thematic information. We present a framework to collect and extract crop type and phenological information from street level imagery using computer vision. During the 2018 growing season, high definition pictures were captured with side-looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed 200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures. At 220 specific parcel locations detailed on the spot crop phenology observations were recorded for 17 crop types. Furthermore, the time span included specific pre-emergence parcel stages, such as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g. green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition model, based on transfer learning with convolutional neural networks (MobileNet). A hypertuning methodology was developed to obtain the best performing model among 160 models. This best model was applied on an independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data collection and suggests avenues for massive data collection via automated classification using computer vision., Comment: 18 pages

The nitrogen-vacancy (NV) center has enabled widespread study of nanoscale nuclear magnetic resonance (NMR) spectroscopy at low magnetic fields. NMR spectroscopy at high magnetic fields significantly improves the technique's spectral resolution, enabling clear identification of closely related chemical species. However, NV-detected NMR is typically performed using AC sensing through electron spin echo envelope modulation (ESEEM), a hyperfine spectroscopic technique that is not feasible at high magnetic fields. Within this paper, we have explored an NV-detected NMR technique for applications of high field NMR. We have demonstrated optically detected magnetic resonance (ODMR) with the NV Larmor frequency of 230 GHz at 8.3 Tesla, corresponding to a proton NMR frequency of 350 MHz. We also demonstrated the first measurement of electron-electron double resonance detected NMR (EDNMR) using the NV center and successfully detected $^{13}C$ nuclear bath spins. The described technique is limited by the longitudinal relaxation time ($T_1$), not the transverse relaxation time ($T_2$). Future applications of the method to perform nanoscale NMR of external spins at 8.3 T and even higher magnetic fields are also discussed., Comment: 17 pages, 4 figures

The "MIcro-Surgical Anastomose Workflow recognition on training sessions" (MISAW) challenge provided a data set of 27 sequences of micro-surgical anastomosis on artificial blood vessels. This data set was composed of videos, kinematics, and workflow annotations described at three different granularity levels: phase, step, and activity. The participants were given the option to use kinematic data and videos to develop workflow recognition models. Four tasks were proposed to the participants: three of them were related to the recognition of surgical workflow at three different granularity levels, while the last one addressed the recognition of all granularity levels in the same model. One ranking was made for each task. We used the average application-dependent balanced accuracy (AD-Accuracy) as the evaluation metric. This takes unbalanced classes into account and it is more clinically relevant than a frame-by-frame score. Six teams, including a non-competing team, participated in at least one task. All models employed deep learning models, such as CNN or RNN. The best models achieved more than 95% AD-Accuracy for phase recognition, 80% for step recognition, 60% for activity recognition, and 75% for all granularity levels. For high levels of granularity (i.e., phases and steps), the best models had a recognition rate that may be sufficient for applications such as prediction of remaining surgical time or resource management. However, for activities, the recognition rate was still low for applications that can be employed clinically. The MISAW data set is publicly available to encourage further research in surgical workflow recognition. It can be found at www.synapse.org/MISAW, Comment: MICCAI2020 challenge report, 36 pages including 15 for supplementary material (complet results for each participating teams), 17 figures