A simple strategy for body estimation from partial-view images

2024
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Virtual try-on and product personalization have become increasingly important in modern online shopping, high-lighting the need for accurate body measurement estimation. Although previous research has advanced in estimating 3D body shapes from RGB images, the task is inherently ambiguous as the observed scale of human subjects in the images depends on two unknown factors: capture distance and body dimensions. This ambiguity is particularly pronounced in partial-view scenarios. To address this challenge, we propose a modular and simple height normalization solution. This solution relocates the subject skeleton to the desired position, thereby normalizing the scale and disentangling the relationship between the two variables. Our experimental results demonstrate that integrating this technique into state-of-the-art human mesh reconstruction models significantly enhances partial body measurement estimation. Additionally, we illustrate the applicability of this approach to multi-view settings, showcasing its versatility.
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US, WA, Bellevue
Amazon Fulfillment Planning & Execution (FPX) Science team within Supply Chain Optimization Technologies (SCOT) Fulfilment Optimization group is seeking a Principal Research Scientist with expertise in Machine Learning and a proven record of solving business problems through scalable ML solutions. Network Planning and Fulfillment Execution tackles some of the most mathematically complex challenges in facility and transportation planning to improve Amazon's operational efficiency worldwide. We own Amazon’s global fulfillment center and transportation topology planning and execution. The team also owns the short-term network planning that determines the optimal flow of customer orders through Amazon fulfillment network. This includes developing sophisticated math models and controllers that assign orders to fulfillment centers to be picked and packed and then planning the optimal ship method in terms of cost, speed and carbon impact to deliver to the customer. These plans drive downstream decisions that are in the billions of dollars. The systems we build are entirely in-house, and are on the cutting edge of both academic and applied research in large scale supply chain planning, optimization, machine learning and statistics. These systems operate at various scales, from real-time decision system that completes thousands of transactions per seconds, to large scale distributed system that optimize Amazon’s fulfillment network. As Amazon continues to build and expand the first party delivery network, this role will be critical to realize this vision. Your tech solution will have large impacts to the physical supply chain of Amazon, and play a key role in improving Amazon consumer business’s long-term profitability. If you are interested in diving into a multi-discipline, high impact space this is the team for you. Key job responsibilities As a Principal Research Scientist within FPX Science team, you will propose and deploy solutions that will likely draw from a range of scientific areas such as supervised, semi-supervised and unsupervised learning, reinforcement learning, advanced statistical modeling, and graph models. You will have an opportunity to be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry, with some of the best product managers, research scientists, statisticians, and software engineers to integrate scientific work into production systems. You will partner with the senior tech leaders in the organization to define the long-term vision of our Network Planning and Fulfillment Execution systems. You will play a key role in developing long term strategic solutions that have business impact beyond the scope of the organization. You will bring deep technical expertise in the area of Machine Learning, and will play an integral part in building Amazon's Fulfillment Optimization systems. Other responsibilities include: • Research and develop machine learning models to solve diverse business problems faced within Network Planning and Fulfillment Execution team. • Drive and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring. • Review and audit modeling processes and results for other scientists, both junior and senior. • Advocate the right ML solutions to business stakeholders, engineering teams, as well as executive level decision makers • You will ensure senior leaders in the organization are up to speed on important trends, tools and technologies and how they will be used to impact the business. A day in the life In this role, you will be a technical leader in machine learning with significant scope, impact, and high visibility. 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Our team directly supports multiple functional areas across Fulfillment Optimization and the research needs of the corresponding product and engineering teams. We tackle some of the most mathematically complex challenges in facility and transportation planning to improve Amazon's operational efficiency worldwide and at a scale that is unique to Amazon. We often seek the opportunity of applying hybrid techniques in the space of Operations Research and Machine Learning to tackle some of our biggest technical challenges. We disambiguate complex supply chain problems and create ML and optimization solutions to solve those problems at scale. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, WA, Seattle
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US, VA, Arlington
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GB, London
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GB, London
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GB, Cambridge
The Artificial General Intelligence team (AGI) has an exciting position for an Applied Scientist with a strong background NLP and Large Language Models to help us develop state-of-the-art conversational systems. As part of this team, you will collaborate with talented scientists and software engineers to enable conversational assistants capabilities to support the use of external tools and sources of information, and develop novel reasoning capabilities to revolutionise the user experience for millions of Alexa customers. Key job responsibilities As an Applied Scientist, you will develop innovative solutions to complex problems to extend the functionalities of conversational assistants . You will use your technical expertise to research and implement novel algorithms and modelling solutions in collaboration with other scientists and engineers. You will analyse customer behaviours and define metrics to enable the identification of actionable insights and measure improvements in customer experience. You will communicate results and insights to both technical and non-technical audiences through written reports, presentations and external publications. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR | London, GBR
GB, Cambridge
The Artificial General Intelligence team (AGI) has an exciting position for an Applied Scientist with a strong background NLP and Large Language Models to help us develop state-of-the-art conversational systems. As part of this team, you will collaborate with talented scientists and software engineers to enable conversational assistants capabilities to support the use of external tools and sources of information, and develop novel reasoning capabilities to revolutionise the user experience for millions of Alexa customers. Key job responsibilities As an Applied Scientist, you will develop innovative solutions to complex problems to extend the functionalities of conversational assistants . You will use your technical expertise to research and implement novel algorithms and modelling solutions in collaboration with other scientists and engineers. You will analyse customer behaviours and define metrics to enable the identification of actionable insights and measure improvements in customer experience. You will communicate results and insights to both technical and non-technical audiences through written reports, presentations and external publications. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR | London, GBR
US, VA, Arlington
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center at AWS is a new strategic team that helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, data scientists, engineers, and solution architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. A key focus of this role is GenAI model customization using techniques such as fine-tuning and continued pre-training to help customers build differentiating solutions with their unique data. Key job responsibilities As a Data Scientist, you will: Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder Provide customer and market feedback to Product and Engineering teams to help define product direction About the team Sales, Marketing and Global Services (SMGS) AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest-growing small- and mid-market accounts to enterprise-level customers, including the public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. The Professional Services team is part of Global Services. About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Denver, CO, USA | Herndon, VA, USA | New York, NY, USA | Santa Clara, CA, USA | Seattle, WA, USA | Washington Dc, DC, USA
GB, London
Re-imagining the realms of what’s possible in advertising. Amazon is re-imagining advertising. Amazon Ads operates at the intersection of eCommerce and advertising and offering a rich array of advertising solutions and audience insights so businesses and brands can create relevant campaigns that produce measurable results. At Amazon Ads, you can build models that impact millions every day. And we’re passionate about solving real-world problems while using cutting-edge machine learning and artificial intelligence to do this. For example, our applied science teams leverage a variety of advanced machine learning and cloud computing techniques to power Amazon's advertising offerings. This includes building algorithms and cloud services using clustering, deep neural networks, and other ML approaches to make ads more relevant while respecting privacy. They develop machine learning models to predict ad outcomes and select the optimal ad for each shopper, context, and advertiser objective, leveraging techniques like multi-task learning, bandit/reinforcement learning, counterfactual estimation, and low-latency extreme ML. The teams also utilize Spark, EMR, and Elasticsearch to extract insights from big data and deliver recommendations to advertisers at scale, continuously improving through offline analysis and impact evaluation. Additionally, they apply generative AI models for dynamic creative optimization and video experimentation and automation. Underpinning these efforts are unique technical challenges, such as operating at unprecedented scale (hundreds of thousands of requests per second with 40ms latency) while respecting privacy and customer trust guarantees, and solving a wide variety of complex computational advertising problems related to traffic quality, viewability, brand safety, and more. Help us take innovation in advertising to the next level. Our teams are based in our fast-growing tech hubs in London and Edinburgh. Learn more about Amazon Ads, employee stories and available opportunities here: https://www.amazon.jobs/content/en/teams/advertising/applied-science-machine-learning-research?ref_=a20m_us_car_lp_asml Key job responsibilities * Design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both analysis and business judgment. * Collaborate with software engineering teams to integrate successful experiments into large-scale, highly complex Amazon production systems. * Promote the culture of experimentation and applied science at Amazon. * Demonstrate ability to meet deadlines while managing multiple projects. * Excel communication and presentation skills working with multiple peer groups and different levels of management * Influence and continuously improve a sustainable team culture that exemplifies Amazon’s leadership principles * Develop a deep and wide understanding of large ad tech solutions to which you will contribute, and how they interact with components owned by other teams. * Anticipate obstacles and look around corners, effectively prioritising work, solving trade-offs and influencing the development of advertising products beyond the scope of your immediate team. We are open to hiring candidates to work out of one of the following locations: Edinburgh, MLN, GBR | London, GBR
CN, 11, Beijing
Amazon Search JP builds features powering product search on the Amazon JP shopping site and expands the innovations to world wide. As an Applied Scientist on this growing team, you will take on a key role in improving the NLP and ranking capabilities of the Amazon product search service. Our ultimate goal is to help customers find the products they are searching for, and discover new products they would be interested in. We do so by developing NLP components that cover a wide range of languages and systems. As an Applied Scientist for Search JP, you will design, implement and deliver search features on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation in NLP and IR to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. You will then integrate these models into the production search engine that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times. Key job responsibilities - Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching, ranking and Search suggestion problems. - Analyzing data and metrics relevant to the search experiences. - Working with teams worldwide on global projects. Your benefits include: - Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers - The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems with tangible customer impact - Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goals We are open to hiring candidates to work out of one of the following locations: Beijing, 11, CHN | Shanghai, 31, CHN