517 research outputs found
Novel View Synthesis of Humans using Differentiable Rendering
We present a new approach for synthesizing novel views of people in new
poses. Our novel differentiable renderer enables the synthesis of highly
realistic images from any viewpoint. Rather than operating over mesh-based
structures, our renderer makes use of diffuse Gaussian primitives that directly
represent the underlying skeletal structure of a human. Rendering these
primitives gives results in a high-dimensional latent image, which is then
transformed into an RGB image by a decoder network. The formulation gives rise
to a fully differentiable framework that can be trained end-to-end. We
demonstrate the effectiveness of our approach to image reconstruction on both
the Human3.6M and Panoptic Studio datasets. We show how our approach can be
used for motion transfer between individuals; novel view synthesis of
individuals captured from just a single camera; to synthesize individuals from
any virtual viewpoint; and to re-render people in novel poses. Code and video
results are available at
https://github.com/GuillaumeRochette/HumanViewSynthesis.Comment: Accepted at IEEE transactions on Biometrics, Behavior, and Identity
Science, 10 pages, 11 figures. arXiv admin note: substantial text overlap
with arXiv:2111.1273
Vitalism and Its Legacy in Twentieth Century Life Sciences and Philosophy
This Open Access book combines philosophical and historical analysis of various forms of alternatives to mechanism and mechanistic explanation, focusing on the 19th century to the present. It addresses vitalism, organicism and responses to materialism and its relevance to current biological science. In doing so, it promotes dialogue and discussion about the historical and philosophical importance of vitalism and other non-mechanistic conceptions of life. It points towards the integration of genomic science into the broader history of biology. It details a broad engagement with a variety of nineteenth, twentieth and twenty-first century vitalisms and conceptions of life. In addition, it discusses important threads in the history of concepts in the United States and Europe, including charting new reception histories in eastern and south-eastern Europe. While vitalism, organicism and similar epistemologies are often the concern of specialists in the history and philosophy of biology and of historians of ideas, the range of the contributions as well as the geographical and temporal scope of the volume allows for it to appeal to the historian of science and the historian of biology generally
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Sonic heritage: listening to the past
History is so often told through objects, images and photographs, but the potential of sounds to reveal place and space is often neglected. Our research project ‘Sonic Palimpsest’1 explores the potential of sound to evoke impressions and new understandings of the past, to embrace the sonic as a tool to understand what was, in a way that can complement and add to our predominant visual understandings. Our work includes the expansion of the Oral History archives held at Chatham Dockyard to include women’s voices and experiences, and the creation of sonic works to engage the public with their heritage. Our research highlights the social and cultural value of oral history and field recordings in the transmission of knowledge to both researchers and the public. Together these recordings document how buildings and spaces within the dockyard were used and experienced by those who worked there. We can begin to understand the social and cultural roles of these buildings within the community, both past and present
Beyond Quantity: Research with Subsymbolic AI
How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately
A review of fine-grained sketch image retrieval based on deep learning
Sketch image retrieval is an important branch of the image retrieval field, mainly relying on sketch images as queries for content search. The acquisition process of sketch images is relatively simple and in some scenarios, such as when it is impossible to obtain photos of real objects, it demonstrates its unique practical application value, attracting the attention of many researchers. Furthermore, traditional generalized sketch image retrieval has its limitations when it comes to practical applications; merely retrieving images from the same category may not adequately identify the specific target that the user desires. Consequently, fine-grained sketch image retrieval merits further exploration and study. This approach offers the potential for more precise and targeted image retrieval, making it a valuable area of investigation compared to traditional sketch image retrieval. Therefore, we comprehensively review the fine-grained sketch image retrieval technology based on deep learning and its applications and conduct an in-depth analysis and summary of research literature in recent years. We also provide a detailed introduction to three fine-grained sketch image retrieval datasets: Queen Mary University of London (QMUL) ShoeV2, ChairV2 and PKU Sketch Re-ID, and list common evaluation metrics in the sketch image retrieval field, while showcasing the best performance achieved for these datasets. Finally, we discuss the existing challenges, unresolved issues and potential research directions in this field, aiming to provide guidance and inspiration for future research
AI-Generated Images as Data Source: The Dawn of Synthetic Era
The advancement of visual intelligence is intrinsically tethered to the
availability of large-scale data. In parallel, generative Artificial
Intelligence (AI) has unlocked the potential to create synthetic images that
closely resemble real-world photographs. This prompts a compelling inquiry: how
much visual intelligence could benefit from the advance of generative AI? This
paper explores the innovative concept of harnessing these AI-generated images
as new data sources, reshaping traditional modeling paradigms in visual
intelligence. In contrast to real data, AI-generated data exhibit remarkable
advantages, including unmatched abundance and scalability, the rapid generation
of vast datasets, and the effortless simulation of edge cases. Built on the
success of generative AI models, we examine the potential of their generated
data in a range of applications, from training machine learning models to
simulating scenarios for computational modeling, testing, and validation. We
probe the technological foundations that support this groundbreaking use of
generative AI, engaging in an in-depth discussion on the ethical, legal, and
practical considerations that accompany this transformative paradigm shift.
Through an exhaustive survey of current technologies and applications, this
paper presents a comprehensive view of the synthetic era in visual
intelligence. A project associated with this paper can be found at
https://github.com/mwxely/AIGS .Comment: 20 pages, 11 figure
Neural function approximation on graphs: shape modelling, graph discrimination & compression
Graphs serve as a versatile mathematical abstraction of real-world phenomena in numerous scientific disciplines. This thesis is part of the Geometric Deep Learning subject area, a family of learning paradigms, that capitalise on the increasing volume of non-Euclidean data so as to solve real-world tasks in a data-driven manner. In particular, we focus on the topic of graph function approximation using neural networks, which lies at the heart of many relevant methods. In the first part of the thesis, we contribute to the understanding and design of Graph Neural Networks (GNNs). Initially, we investigate the problem of learning on signals supported on a fixed graph. We show that treating graph signals as general graph spaces is restrictive and conventional GNNs have limited expressivity. Instead, we expose a more enlightening perspective by drawing parallels between graph signals and signals on Euclidean grids, such as images and audio. Accordingly, we propose a permutation-sensitive GNN based on an operator analogous to shifts in grids and instantiate it on 3D meshes for shape modelling (Spiral Convolutions). Following, we focus on learning on general graph spaces and in particular on functions that are invariant to graph isomorphism. We identify a fundamental trade-off between invariance, expressivity and computational complexity, which we address with a symmetry-breaking mechanism based on substructure encodings (Graph Substructure Networks). Substructures are shown to be a powerful tool that provably improves expressivity while controlling computational complexity, and a useful inductive bias in network science and chemistry. In the second part of the thesis, we discuss the problem of graph compression, where we analyse the information-theoretic principles and the connections with graph generative models. We show that another inevitable trade-off surfaces, now between computational complexity and compression quality, due to graph isomorphism. We propose a substructure-based dictionary coder - Partition and Code (PnC) - with theoretical guarantees that can be adapted to different graph distributions by estimating its parameters from observations. Additionally, contrary to the majority of neural compressors, PnC is parameter and sample efficient and is therefore of wide practical relevance. Finally, within this framework, substructures are further illustrated as a decisive archetype for learning problems on graph spaces.Open Acces
An Outlook into the Future of Egocentric Vision
What will the future be? We wonder! In this survey, we explore the gap
between current research in egocentric vision and the ever-anticipated future,
where wearable computing, with outward facing cameras and digital overlays, is
expected to be integrated in our every day lives. To understand this gap, the
article starts by envisaging the future through character-based stories,
showcasing through examples the limitations of current technology. We then
provide a mapping between this future and previously defined research tasks.
For each task, we survey its seminal works, current state-of-the-art
methodologies and available datasets, then reflect on shortcomings that limit
its applicability to future research. Note that this survey focuses on software
models for egocentric vision, independent of any specific hardware. The paper
concludes with recommendations for areas of immediate explorations so as to
unlock our path to the future always-on, personalised and life-enhancing
egocentric vision.Comment: We invite comments, suggestions and corrections here:
https://openreview.net/forum?id=V3974SUk1
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