10 accepted papers
Complex causal systems with interdependent variables require inference from heterogeneous observations that vary in spatial resolution, temporal frequency, and noise characteristics due to data acquisition constraints. Existing multi-modal fusion approaches assume uniform data quality or complete ob…
We present PAMotion, a physics-aware diffusion framework for generating realistic full-body human interactions with multiple objects.Existing diffusion-based methods that jointly synthesize human and object motions often struggle to capture the intricate physical interactions--especially those invol
We present S³-TIR (Coupled Spatial-Spectral Splatting), a physics-inspired framework for high-fidelity thermal infrared (TIR) novel view synthesis. Existing 3D Gaussian splatting methods struggle to capture the unique duality of TIR imagery: the coexistence of diffusive smoothness from heat conducti
Despite their success, Segment Anything Models (SAMs) experience significant performance drops on severely degraded, low-quality images, limiting their effectiveness in real-world scenarios. To address this, we propose GleSAM, which utilizes Generative Latent space Enhancement to boost robustness on…
Datasets play a pivotal role in training visual models, facilitating the development of abstract understandings of visual features through diverse image samples and multidimensional attributes. However, in the realm of aesthetic evaluation of artistic images, datasets remain relatively scarce. Exist…
Point-supervised vision tasks, including detection and segmentation, aiming to learn a network that transforms from points to pseudo labels, have attracted much attention in recent years. However, the lack of precise object size and boundary annotations in the point-supervised condition results in a…
Image aesthetic evaluation is a highly prominent research domain in the field of computer vision. In recent years, there has been a proliferation of datasets and corresponding evaluation methodologies for assessing the aesthetic quality of photographic works, leading to the establishment of a relati…
In this paper we present ShapeMatcher a unified self-supervised learning framework for joint shape canonicalization segmentation retrieval and deformation. Given a partially-observed object in an arbitrary pose we first canonicalize the object by extracting point-wise affine invariant features disen…
Unsupervised learning is a challenging task due to the lack of labels. Multiple Object Tracking (MOT), which inevitably suffers from mutual object interference, occlusion, etc., is even more difficult without label supervision. In this paper, we explore the latent consistency of sample features acro…
In this paper, we propose a novel and specially designed method for piecewise dense monocular depth estimation in dynamic scenes. We utilize spatial relations between neighboring superpixels to solve the inherent relative scale ambiguity (RSA) problem and smooth the depth map. However, directly esti…