7 research outputs found
CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting
With the onset of diffusion-based generative models and their ability to
generate text-conditioned images, content generation has received a massive
invigoration. Recently, these models have been shown to provide useful guidance
for the generation of 3D graphics assets. However, existing work in
text-conditioned 3D generation faces fundamental constraints: (i) inability to
generate detailed, multi-object scenes, (ii) inability to textually control
multi-object configurations, and (iii) physically realistic scene composition.
In this work, we propose CG3D, a method for compositionally generating scalable
3D assets that resolves these constraints. We find that explicit Gaussian
radiance fields, parameterized to allow for compositions of objects, possess
the capability to enable semantically and physically consistent scenes. By
utilizing a guidance framework built around this explicit representation, we
show state of the art results, capable of even exceeding the guiding diffusion
model in terms of object combinations and physics accuracy
MIME: Minority Inclusion for Majority Group Enhancement of AI Performance
Several papers have rightly included minority groups in artificial
intelligence (AI) training data to improve test inference for minority groups
and/or society-at-large. A society-at-large consists of both minority and
majority stakeholders. A common misconception is that minority inclusion does
not increase performance for majority groups alone. In this paper, we make the
surprising finding that including minority samples can improve test error for
the majority group. In other words, minority group inclusion leads to majority
group enhancements (MIME) in performance. A theoretical existence proof of the
MIME effect is presented and found to be consistent with experimental results
on six different datasets. Project webpage:
https://visual.ee.ucla.edu/mime.htm
Making Thermal Imaging More Equitable and Accurate: Resolving Solar Loading Biases
Thermal cameras and thermal point detectors are used to measure the
temperature of human skin. These are important devices that are used everyday
in clinical and mass screening settings, particularly in an epidemic.
Unfortunately, despite the wide use of thermal sensors, the temperature
estimates from thermal sensors do not work well in uncontrolled scene
conditions. Previous work has studied the effect of wind and other environment
factors on skin temperature, but has not considered the heating effect from
sunlight, which is termed solar loading. Existing device manufacturers
recommend that a subject who has been outdoors in sun re-acclimate to an indoor
environment after a waiting period. The waiting period, up to 30 minutes, is
insufficient for a rapid screening tool. Moreover, the error bias from solar
loading is greater for darker skin tones since melanin absorbs solar radiation.
This paper explores two approaches to address this problem. The first approach
uses transient behavior of cooling to more quickly extrapolate the steady state
temperature. A second approach explores the spatial modulation of solar
loading, to propose single-shot correction with a wide-field thermal camera. A
real world dataset comprising of thermal point, thermal image, subjective, and
objective measurements of melanin is collected with statistical significance
for the effect size observed. The single-shot correction scheme is shown to
eliminate solar loading bias in the time of a typical frame exposure (33ms)
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Diverse Patient Heart Rate Monitoring Using Consumer Camera Systems
Real world scenes and objects have diverse visual appearance. Such diversity stems from the fundamental physics in how light interacts with matter, across different weather conditions, object types, and even people. These appearance variations mesmerize human beings, but puzzle artificial vision systems, which cannot generalize to such diversity. Through this thesis, we look at one such case of biased performance over diversity- camera based remote heart rate (HR) estimation. HR is an essential clinical measure for the assessment of cardiorespiratory instability. The growing telemedicine market opens up the urgent requirement for scalable yet affordable remote HR estimation. However, existing computer vision methods that estimate HR from facial videos exhibit biased performance against dark skin tones. This is a major concern, since communities of color are disproportionately affected by both COVID-19 and cardiovascular disease. We identify and model the origin of this bias and present a novel physics-driven algorithm that boosts performance on darker skin tones in our reported data. We assess the performance of our method through the creation of the first telemedicine-focused remote vital signs dataset, the VITAL dataset. 432 videos (~864 minutes) of 54 subjects with diverse skin tones are recorded under realistic scene conditions with corresponding vital sign data. Our method mitigates errors due environmental conditions and imparts unbiased performance gains across skin tones, setting the stage for making non-contact HR sensing technologies a viable reality for patients across skin tones