A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses

We tackle the problem of learning a rotation invariant latent factor model when the training data is comprised of lower-dimensional projections of the original feature space. The main goal is the discovery of a set of 3-D bases poses that can characterize the manifold of primitive human motions, or movemes, from a training set of 2-D projected poses obtained from still images taken at various camera angles. The proposed technique for basis discovery is data-driven rather than hand-designed. The learned representation is rotation invariant, and can reconstruct any training instance from multiple viewing angles. We apply our method to modeling human poses in sports (via the Leeds Sports Dataset), and demonstrate the effectiveness of the learned bases in a range of applications such as activity classification, inference of dynamics from a single frame, and synthetic representation of movements

Vision for Social Robots: Human Perception and Pose Estimation

In order to extract the underlying meaning from a scene captured from the surrounding world in a single still image, social robots will need to learn the human ability to detect different objects, understand their arrangement and relationships relative both to their own parts and to each other, and infer the dynamics under which they are evolving. Furthermore, they will need to develop and hold a notion of context to allow assigning different meanings (semantics) to the same visual configuration (syntax) of a scene. The underlying thread of this Thesis is the investigation of new ways for enabling interactions between social robots and humans, by advancing the visual perception capabilities of robots when they process images and videos in which humans are the main focus of attention. First, we analyze the general problem of scene understanding, as social robots moving through the world need to be able to interpret scenes without having been assigned a specific preset goal. Throughout this line of research, i) we observe that human actions and interactions which can be visually discriminated from an image follow a very heavy-tailed distribution; ii) we develop an algorithm that can obtain a spatial understanding of a scene by only using cues arising from the effect of perspective on a picture of a person’s face; and iii) we define a novel taxonomy of errors for the task of estimating the 2D body pose of people in images to better explain the behavior of algorithms and highlight their underlying causes of error. Second, we focus on the specific task of 3D human pose and motion estimation from monocular 2D images using weakly supervised training data, as accurately predicting human pose will open up the possibility of richer interactions between humans and social robots. We show that when 3D ground-truth data is only available in small quantities, or not at all, it is possible to leverage knowledge about the physical properties of the human body, along with additional constraints related to alternative types of supervisory signals, to learn models that can regress the full 3D pose of the human body and predict its motions from monocular 2D images. Taken in its entirety, the intent of this Thesis is to highlight the importance of, and provide novel methodologies for, social robots' ability to interpret their surrounding environment, learn in a way that is robust to low data availability, and generalize previously observed behaviors to unknown situations in a similar way to humans.</p

Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation

We propose a new method to analyze the impact of errors in algorithms for multi-instance pose estimation and a principled benchmark that can be used to compare them. We define and characterize three classes of errors - localization, scoring, and background - study how they are influenced by instance attributes and their impact on an algorithm's performance. Our technique is applied to compare the two leading methods for human pose estimation on the COCO Dataset, measure the sensitivity of pose estimation with respect to instance size, type and number of visible keypoints, clutter due to multiple instances, and the relative score of instances. The performance of algorithms, and the types of error they make, are highly dependent on all these variables, but mostly on the number of keypoints and the clutter. The analysis and software tools we propose offer a novel and insightful approach for understanding the behavior of pose estimation algorithms and an effective method for measuring their strengths and weaknesses.Comment: Project page available at http://www.vision.caltech.edu/~mronchi/projects/PoseErrorDiagnosis/; Code available at https://github.com/matteorr/coco-analyze; published at ICCV 1

Towards COP27: The Water-Food-Energy Nexus in a Changing Climate in the Middle East and North Africa

Due to its low adaptability to climate change, the MENA region has become a "hot spot". Water scarcity, extreme heat, drought, and crop failure will worsen as the region becomes more urbanized and industrialized. Both water and food scarcity are made worse by civil wars, terrorism, and political and social unrest. It is unclear how climate change will affect the MENA water–food–energy nexus. All of these concerns need to be empirically evaluated and quantified for a full climate change assessment in the region. Policymakers in the MENA region need to be aware of this interconnection between population growth, rapid urbanization, food safety, climate change, and the global goal of lowering greenhouse gas emissions (as planned in COP27). Researchers from a wide range of disciplines have come together in this SI to investigate the connections between water, food, energy, and climate in the region. By assessing the impacts of climate change on hydrological processes, natural disasters, water supply, energy production and demand, and environmental impacts in the region, this SI will aid in implementation of sustainable solutions to these challenges across multiple spatial scales

The concept of 'Genetic Modification' in a Descriptive Translation Study (DTS) of an English-Spanish corpus of Popular Science Books on Genetic Engineering: Denominative Variation, Semantic Prosody and Ideological Aspects of Translation Strategies

El objetivo general consiste en examinar el concepto de 'modificación genética' a través de tres fenómenos lingüísticos: la variación denominativa, la prosodia semántica y los aspectos ideológicos de las principales estrategias de traducción. Para estudiar la variación denominativa se han seleccionado dos términos técnicos 'DNA' y 'gene/s' y dos subtécnicos 'food/s' y 'crop/s'. Para el estudio de la prosodia semántica se han analizado las concordancias de 'genetic' + N y 'genetically'`+ Adj. La comparación de las variantes denominativas y las prosodias semánticas en un corpus paralelo inglés-español de ingenería genética arrojan resultados sobre los aspectos ideológicos de las principales estrategias de traducción encontradas en el corpus.Departamento de Filología Ingles