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Harmony4D: A Video Dataset for In-The-Wild Close Human Interactions - Supplementary Materials - Rawal Khirodkar
For video demos of Harmony4D, please visit: Harmony4D Website. Please do not share the dataset with anyone as it is not publicly available yet. Harmony4D is a 75-minute video dataset collected using over 20 eqidistant, synchronized GoPro cameras. It consists of 1.66M images and 3.32M human instances, divided into 1.28M images for We manually clipped the videos into 208 sequences across 6 different activities, ensuring each sequence is at least 5 seconds (100 frames) long for temporal continuity. The 2D bboxes are derived from projected SMPL human vertices.
Harmony4D: A Video Dataset for In-The-Wild Close Human Interactions
Understanding how humans interact with each other is key to building realistic multi-human virtual reality systems. This area remains relatively unexplored due to the lack of large-scale datasets. Recent datasets focusing on this issue mainly consist of activities captured entirely in controlled indoor environments with choreographed actions, significantly affecting their diversity.
VFIMamba: Video Frame Interpolation with State Space Models
In particular, on the X-TEST dataset, VFIMamba demonstrates a noteworthy improvement of 0.80 dB for 4K frames and 0.96 dB for 2K frames.
Trajectory Flow Matching with Applications to Clinical Time Series Modeling
Neural stochastic differential equations (Neural SDEs) are an attractive modeling technique for this problem, which parameterize the drift and diffusion terms of an SDE with neural networks.
Self-Calibrating Conformal Prediction
In machine learning, model calibration and predictive inference are essential for producing reliable predictions and quantifying uncertainty to support decision-making.
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems.
Convolutional Neural Operators for robust and accurate learning of PDEs
Here, we present novel adaptations for convolutional neural networks to demonstrate that they are indeed able to process functions as inputs and outputs.
MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views Y uedong Chen
Diffusion (SVD) model, where these features then act as pose and visual cues to guide the denoising process and produce photorealistic 3D-consistent views. Our model is end-to-end trainable and supports rendering arbitrary views with as few as 5 sparse input views. To evaluate MVSplat360's performance, we introduce a new benchmark using the challenging DL3DV -10K dataset, where
