12 accepted papers
While text-to-video (T2V) generation has achieved remarkable progress in photorealism, generating intent-aligned videos that faithfully obey physics principles remains a core challenge. In this work, we systematically study Newtonian motion-controlled text-to-video generation and evaluation, emphasi
The essence of video lies in pixel dynamics: motion, state transitions, and the flow of visual information across frames. Video Large Language Models (LLMs) have rapidly become the dominant paradigm for video understanding in computer vision, sophisticated multimodal reasoning over complex, long-for…
We present UniTrack, a plug-and-play graph-theoretic loss function designed to significantly enhance multi-object tracking (MOT) performance by directly optimizing tracking-specific objectives through unified differentiable learning. Unlike prior graph-based MOT methods that redesign tracking archit…
Diffusion models have proven effective at generating high-quality images from learned distributions, but their application to the temporal domain, especially for driving scenarios, remains underexplored. Our work addresses key challenges in existing simulations, such as limited data quality, diversi
This paper introduces MOTE (MOre Than meets the Eye), a novel multi-object tracking (MOT) algorithm designed to address the challenges of tracking occluded objects. By integrating deformable detection transformers with a custom disocclusion matrix, MOTE significantly enhances the ability to track ob…
Evaluating autonomous ground vehicles requires evaluating their mobility performance. Since autonomous vehicles are envisioned to make decisions in a variety of situations and environments too diverse to practically assess with only physical testing, their development, and evaluation will necessaril…
Factor analysis methods have been widely used in neuroimaging to transfer high dimensional imaging data into low dimensional, ideally interpretable representations. However, most of these methods overlook the highly nonlinear and complex temporal dynamics of neural processes when factorizing their i…
We introduce deep switching auto-regressive factorization (DSARF), a deep generative model for spatio-temporal data with the capability to unravel recurring patterns in the data and perform robust short- and long-term predictions. Similar to other factor analysis methods, DSARF approximates high dim…
In this paper, we propose a computationally tractable and theoretically supported non-linear low-dimensional generative model to represent real-world data in the presence of noise and sparse outliers. The non-linear low-dimensional manifold discovery of data is done through describing a joint distri…
Image-based generative methods, such as generative adversarial networks (GANs) have already been able to generate realistic images with much context control, specially when they are conditioned. However, most successful frameworks share a common procedure which performs an image-to-image translation…
All modern emotion theoretical views assume a role for peripheral physiological changes during emotional experiences. In this paper, we explored the correlation between autonomically-mediated changes in multimodal bodily signals and discrete emotional states. In order to fully exploit the informatio…
Automatic tracking of rodents' behaviors over time in their home cages is of great interest in psycho-physiological studies. The commercially-available animal monitoring systems use RGB videos or bio-potential signals to monitor behaviors of animals when exploring their surroundings. The based model…